Source: https://patents.google.com/patent/JP6245875B2/en
Timestamp: 2020-04-07 08:34:22
Document Index: 420737613

Matched Legal Cases: ['art 102', 'art 103', 'art 104', 'art 105', 'art 106', 'art 107', 'art 108', 'art 109', 'art 110', 'art 111']

JP6245875B2 - Lens dirt detection device and lens dirt detection method - Google Patents
Lens dirt detection device and lens dirt detection method Download PDF
JP6245875B2
JP6245875B2 JP2013155552A JP2013155552A JP6245875B2 JP 6245875 B2 JP6245875 B2 JP 6245875B2 JP 2013155552 A JP2013155552 A JP 2013155552A JP 2013155552 A JP2013155552 A JP 2013155552A JP 6245875 B2 JP6245875 B2 JP 6245875B2
JP2013155552A
JP2015026987A (en
克行 中村
2013-07-26 Application filed by クラリオン株式会社 filed Critical クラリオン株式会社
2013-07-26 Priority to JP2013155552A priority Critical patent/JP6245875B2/en
2015-02-05 Publication of JP2015026987A publication Critical patent/JP2015026987A/en
2017-12-13 Publication of JP6245875B2 publication Critical patent/JP6245875B2/en
The present invention relates to a system for detecting lens contamination such as an in-vehicle camera.
In order to avoid accidents such as collision between vehicles and collision between people and vehicles, the situation around the vehicle is monitored with a camera, and when a danger is detected, an alarm is output to the driver and the vehicle behavior itself is automatically controlled. Technology to make progress. In a system in which the camera is installed outside the vehicle, the camera lens is easily contaminated by bad weather, mud, dust, etc., and in some cases, the contamination may cause a false alarm, which itself may lead to a decrease in safety. Therefore, a system for automatically detecting lens dirt and removing the lens dirt by blowing air or water has been put into practical use, but such a system is generally expensive. Therefore, when the lens contamination progresses, it is preferable to temporarily stop the vehicle peripheral situation recognition operation to prevent false alarms and suppress unnecessary safety degradation.
For example, Patent Literature 1 discloses a method for determining water droplet adhesion on a lens surface from a difference image between a background image and an input image.
JP 2006-254206 A
Patent Document 1 described above discloses a method of performing water droplet determination using a difference image. However, since water droplets have the property of transmitting through the background, the background image remains and looks like water droplets (for example, a difference due to a change in the background image is detected as a water droplet, or one of the background images May be detected as the edge of a water droplet). Therefore, it is difficult to accurately determine whether or not the presence of water droplets has really emerged from the difference image alone.
An object of the present invention is to accurately determine the adhesion of various types of dirt including permeable dirt such as water droplets to a camera.
An image can be considered as a composite wave formed by overlapping a plurality of waves (shades) having different periods and amplitudes. An image with water droplets attached (that is, an image taken through a lens with water droplets attached) causes image blurring and other changes compared to when the water droplets are not attached. It means that The present invention pays attention to the fact that a large change occurs in the composite wave of the image as the lens dirt of the camera progresses (for example, the amount of water droplets adhering to the lens), and each image frequency component constituting the composite wave The purpose of this method is to determine the presence or absence of camera lens contamination without being affected by the background image by extracting changes in the frequency components and analyzing changes in the magnitude relationship of the frequency components (ie, the distribution of image frequency power). And
According to the exemplary embodiment of the present invention, it is possible to accurately determine the adhesion of various types of dirt including permeable dirt such as water droplets to the camera.
It is a functional block diagram which shows the example of a structure of the external field recognition apparatus of Example 1 of this invention. It is explanatory drawing of the area division process by the image frequency analysis part of Example 1 of this invention. It is explanatory drawing which shows another example of the area | region division process by the image frequency analysis part of Example 1 of this invention. It is explanatory drawing which shows the example of the calculation method of the image frequency by the image frequency analysis part of Example 1 of this invention. It is explanatory drawing which shows the example of the calculation method of the average frequency power by the image frequency analysis part of Example 1 of this invention. It is explanatory drawing which shows the example of the threshold value comparison method in the sample block of the image which the raindrop of Example 1 of this invention does not adhere. It is explanatory drawing which shows the example of the threshold value comparison method in the sample block of the image to which the raindrop of Example 1 of this invention has adhered. It is explanatory drawing which shows the example of operation | movement of the raindrop score calculation part of Example 1 of this invention. It is explanatory drawing which shows the example of operation | movement of the raindrop area | region information generation part of Example 1 of this invention. It is explanatory drawing which shows the example of operation | movement of the raindrop determination part of Example 1 of this invention. It is explanatory drawing which shows the example of operation | movement of the raindrop determination part of Example 2 of this invention. It is a functional block diagram which shows the example of a structure of the raindrop detection part of Example 3 of this invention. It is explanatory drawing which shows the example of operation | movement of the 1st image frequency analysis part of Example 3 of this invention. It is explanatory drawing which shows the example of operation | movement of the 1st raindrop score calculation part which considered the peripheral region of Example 3 of this invention. It is explanatory drawing which shows the thinning of the process area | region by the 1st raindrop score calculation part of Example 3 of this invention. It is a functional block diagram which shows the example of a structure of the raindrop detection part of Example 4 of this invention. It is explanatory drawing which shows the example of a setting of the image observation point in the image distortion analysis part of Example 4 of this invention. It is explanatory drawing which shows the detection processing method of the image distortion analysis part of Example 4 of this invention. It is explanatory drawing which shows the example of operation | movement of the 3rd raindrop score calculation part of Example 4 of this invention. It is explanatory drawing which shows the example of operation | movement of the raindrop determination part in the case of implementing image distortion analysis in Example 4 of this invention. It is a functional block diagram which shows an example of the external field recognition apparatus of Example 5 of this invention. It is explanatory drawing which shows an example of the installation position of infrared LED in Example 5 of this invention. It is a functional block diagram which shows an example of the external field recognition apparatus of Example 6 of this invention. It is a flowchart which shows an example of operation | movement of the external field recognition apparatus of Example 6 of this invention.
In the present embodiment, an example of the raindrop detection unit 101 mounted on the external environment recognition apparatus 100 will be described.
FIG. 1 is a functional block diagram illustrating an example of the configuration of the external environment recognition device 100 according to the first embodiment of the present invention.
The external environment recognition apparatus 100 inputs an image signal of an in-vehicle camera 111 attached to the body of the own vehicle (not shown), performs predetermined image signal processing, recognizes the surrounding state of the own vehicle, and uses the recognition result as the own vehicle. It is an apparatus which outputs to the control part of this, and also alert | reports to a driver.
Recognizing the surrounding state of the host vehicle is an analysis process of camera images taken in the vicinity of the host vehicle and a distant surrounding space, the presence or absence of other vehicles and pedestrians including motorcycles and bicycles, It is to recognize the presence or absence of an obstacle that hinders parking. In addition, other vehicles and pedestrians including motorcycles and bicycles in the vicinity and far away of the host vehicle can detect a sudden approach and predict a collision with the host vehicle, and can also predict a collision between the host vehicle and an obstacle. This is included in the recognition of the surrounding state of the vehicle.
The external environment recognition apparatus 100 includes a raindrop detection unit 101, a surrounding state recognition unit 106, an alarm processing unit 110, and a driver notification unit 109. Furthermore, the external environment recognition apparatus 100 may include an in-vehicle camera 111, a control unit 112, and a memory 113. The in-vehicle camera 111 captures an image around the host vehicle. The pixel value data constituting the photographed image is temporarily stored in the memory 113 via the signal bus and input to the raindrop detection unit 101. The control unit 112 controls input / output of an image between the in-vehicle camera 111 and the raindrop detection unit 101, and input / output of recognition result information and an alarm signal between the external environment recognition device 100 and the own vehicle control unit 114. .
The own vehicle control unit 114 executes control of the own vehicle (for example, steering, deceleration, or stop) based on the recognition result of the surrounding state of the own vehicle by the external environment recognition device 100.
The raindrop detection unit 101 is a block that predicts the presence or absence of raindrops on the lens surface of the in-vehicle camera 111 from the image input from the in-vehicle camera 111, and divides the image input from the in-vehicle camera 111 into a predetermined area. An image frequency analysis unit 102 for obtaining an image frequency for each, a raindrop score calculation unit 103 for calculating the reliability of raindrop adhesion as a score value for each divided region from the obtained image frequency, and a region for one image (one frame) A raindrop area information generation unit 104 that creates a raindrop score map, and a raindrop determination unit 105 that outputs raindrop determination information indicating the presence or absence of raindrop adhesion to the lens surface for each image from the raindrop score map.
The peripheral state recognition unit 106 recognizes the peripheral state of the vehicle with a dirt diagnosis unit 108 that diagnoses whether the lens surface of the in-vehicle camera 111 is dirty due to rain, snow, mud, cloudiness, or the like. And a warning processing unit 110 that transmits the recognition result information and the warning signal output from the surrounding recognition unit 107 to the host vehicle control unit 114. In addition, when the lens surface of the in-vehicle camera 111 is dirty due to rain, mud, cloudiness, etc., the dirt diagnosis unit 108 is in a state where accurate image recognition processing is difficult for the peripheral recognition unit 107 and erroneous recognition is likely to occur. Therefore, the FAIL signal is output to stop the operation of the peripheral recognition unit 107 temporarily or continuously. Further, dirt notification information indicating that the periphery recognition unit 107 is temporarily or continuously stopped is output to the driver notification unit 109, and display means such as a display separately provided outside (see FIG. 23) is provided. Through the driver.
The raindrop detection unit 101 including the image frequency analysis unit 102 shown in FIG. 1, the peripheral state recognition unit 106 including the dirt diagnosis unit 108, the alarm processing unit 110, and the driver notification unit 109 are realized by dedicated logic circuits. However, it may be realized by a general-purpose processor executing a program. In this case, the external environment recognition apparatus 100 includes at least a control unit 112 and a memory 113, and programs for realizing the above-described units are loaded into the memory 113, and the control unit 112 (that is, a processor) executes these programs. The above-described units are realized. In this case, in the following description, the processing executed by each of the above units is actually executed by the control unit 112.
The raindrop detection unit 101 of the present invention can be used to detect various types of dirt such as rain, mud, and cloudiness. This embodiment will be described using an example of raindrop detection.
Hereinafter, the raindrop detection unit 101 will be described.
FIG. 2 is an explanatory diagram illustrating region division processing performed by the image frequency analysis unit 102 according to the first embodiment of this invention.
FIG. 2A is an example of an image 200 taken by mounting the in-vehicle camera 111 on the upper bumper behind the host vehicle. In the image, a road surface condition 205 behind the host vehicle including other vehicles and the scenery behind the vehicle, a light-shielding plate 201 that is integrated with the in-vehicle camera 111 and prevents unnecessary external light such as sunlight from being reflected, The upper end 202 (finisher) of the body, the bumper 203, and the license plate 204 are reflected.
FIG. 2B is a diagram illustrating an example of region division processing of the image frequency analysis unit 102. The image frequency analysis unit 102 divides the space of one frame image captured by the in-vehicle camera 111 into regions of a predetermined image size, and obtains an image frequency for each divided region. FIG. 2B shows a total of 80 images (M = 10, N = 8) of 320 (horizontal) × 256 (vertical) pixel images 200 each having 32 × 32 pixels (xa × ya). In this example, the area 206 is divided.
Note that the mounting position of the in-vehicle camera 111 illustrated in FIG. 2A is an example, and an image captured by the in-vehicle camera 111 installed at another position of the host vehicle may be input to the raindrop detection unit 101.
Furthermore, the input image size shown in FIG. 2B, the image size of one area after area division and the number of area divisions associated therewith are only examples, and both the image size and the number of area divisions are set to arbitrary values. can do.
FIG. 3 is an explanatory diagram illustrating another example of the area division processing by the image frequency analysis unit 102 according to the first embodiment of this invention.
FIG. 2B shows an example in which the entire surface of the image 200 is an area division target, but the raindrop detection area may be set to an arbitrary part or a plurality of parts according to the use of the peripheral state recognition unit 106. good.
For example, FIG. 3A shows an example in which a portion where only the road surface is reflected is set in the raindrop detection region 300. FIG. 3B is an example in which a part of the host vehicle and a part excluding the reflection of the light-shielded version of the in-vehicle camera 111 are set in the raindrop detection area 301. FIG. 3C shows an example in which the lanes drawn on the road are easily reflected in two raindrop detection areas (302 and 303).
2 and 3 show an example in which a plurality of divided areas are set in the image, but only one divided area having a predetermined size may be set in the image.
FIG. 4 is an explanatory diagram illustrating an example of an image frequency calculation method performed by the image frequency analysis unit 102 according to the first embodiment of this invention.
For the calculation of the image frequency, a discrete Fourier transform (hereinafter referred to as DFT) is used. The discrete Fourier transform is an arithmetic method generally known as a method of converting a digital signal (discrete signal) sampled on a predetermined time axis into a frequency spectrum representing a frequency signal distribution. Hereinafter, calculation of an image frequency by DFT will be described.
An image is a composite wave formed by overlapping a plurality of waves, and the density (pixel value) of a certain input image can be decomposed into frequency components from a low frequency to a high frequency by DFT. The frequency here is a frequency of a spatial change of the pixel value and is generally called a spatial frequency or an image frequency. Assuming that the image is a combination of gray and gray waves in two directions, the horizontal direction and the vertical direction, the image frequency component obtained by DFT can also be obtained as a two-dimensional result in the horizontal and vertical directions.
The idea of an ideal Fourier transform is based on obtaining an infinitely continuous image signal and obtaining a frequency component. However, in actual signal processing, a function calculation based on infinite signal input is not realistic, and therefore, a discrete Fourier transform of a finite size is executed by cutting out all or a part of one image area. The image size to be cut out is arbitrarily selected according to which region the image frequency is to be examined.
Next, a specific procedure of DFT used in this embodiment will be described. The following formula 1 is used for DFT of two-dimensional data such as an image. By performing DFT of Formula 1 on an image composed of M (horizontal direction) × N (vertical direction) pixels, M (horizontal direction) × N (vertical direction), that is, the same number as the number of input pixels. Image frequency data is obtained.
Note that x and y are values of spatial coordinates of the image, that is, coordinate values indicating the spatial position of each pixel included in the input image. x is a coordinate value indicating the horizontal position of each pixel, and y is a coordinate value indicating the vertical position of each pixel.
k and l are values indicating frequency space coordinates. k indicates the frequency position in the horizontal direction, and l indicates the frequency position in the vertical direction.
f (x, y) is a function indicating the pixel value at the position specified by the horizontal coordinate value x and the vertical coordinate value y in the image.
W M xk is a coefficient for extracting a horizontal frequency component per period existing in M pixel signals (discrete signals) in the horizontal direction.
The coefficient x represents the horizontal pixel position (x = 0 to M−1), and the coefficient k represents the horizontal frequency component position (k = 0 to M−1).
W N yl is a coefficient for extracting a vertical frequency component per period existing in N pixel signals (discrete signals) in the vertical direction.
The coefficient y represents the vertical pixel position (y = 0 to N−1), and the coefficient l represents the vertical frequency component position (l = 0 to N−1).
F (k, l) is a function indicating image frequency data specified by a horizontal coordinate value k and a vertical coordinate value l.
Furthermore, Formula 4 is used to perform conversion to frequency power so that comparison between frequency spectra can be easily performed. The unit of frequency power is dB (decibel).
It is known that an image with raindrops is blurred compared to a case without raindrops. For example, the background reflected in the image, the edges of other vehicles and surrounding buildings may be blurred due to raindrops, or may disappear in some cases. When the image is blurred, the image frequency is lower than that when the image is not blurred. That is, an image with raindrops has a characteristic that the image frequency is low, and an image without raindrops has a characteristic that the image frequency is high. The present invention uses this characteristic to detect the presence of raindrops by examining the image frequency.
When one image (32 × 32 pixels) after region division shown in FIG. 4A is input and DFT calculation is performed, the DC (direct current) component of the image is used as shown in FIG. Four image frequencies of quadrant A, quadrant B, quadrant C, and quadrant D around a certain four pixel 403 are obtained. However, due to the nature of DFT, the image frequencies of quadrant A, quadrant B, quadrant C, and quadrant D are point-symmetric with respect to the DC component coordinates. That is, since the image frequencies of quadrant B, quadrant C, and quadrant D can be obtained from the image frequency of quadrant A, only 16 × 16 data (hereinafter, sample blocks) of quadrant A can be referred to as the image frequency. Good. Further, as shown in FIG. 4C, the image frequency data obtained by DFT is composed of one DC component value at the lower left and other AC component values 404. The horizontal direction of the image frequency data indicates the horizontal frequency component, the left side indicates the low frequency, and the right side indicates the high frequency. The vertical direction indicates the vertical frequency component, the lower side indicates the low frequency, and the upper side indicates the high frequency.
FIG. 5 is an explanatory diagram illustrating an example of an average frequency power calculation method performed by the image frequency analysis unit 102 according to the first embodiment of this invention.
As shown in FIG. 5A, in order to obtain the average frequency power, the image frequency analysis unit 102 further divides the sample block into 16 small blocks (from the small block a500 to the small block p501). Each small block corresponds to a frequency range including 4 × 4 (ie, 16) frequency space coordinates. That is, the frequency power corresponding to 4 × 4 frequency space coordinates is calculated for each small block by Equations 1-4. Next, the image frequency analysis unit 102 obtains the average frequency power of the small blocks by averaging the frequency power corresponding to 4 × 4 frequency space coordinates for each small block.
FIG. 5B is an example of a result of obtaining the average frequency power for each small block, which is a sample block having an image with no raindrops attached thereto. Average frequency powers 502 to 503 shown in FIG. 5B are examples of average frequency powers obtained for the small block a500 to the small block p501 of the image where no raindrops are attached, respectively. In this example, the average frequency power 502 of the small block a500 corresponding to the lowest frequency range in both the horizontal frequency component and the vertical frequency component is “130”, and both the horizontal frequency component and the vertical frequency component are in the highest frequency range. The average frequency power 503 of the small block p501 is “110”.
FIG. 5C shows an example of a result of obtaining an average frequency power for each small block, which is a sample block having an image with raindrops attached thereto. Average frequency powers 502 to 503 shown in FIG. 5C are examples of average frequency powers obtained for the small block a500 to the small block p501 of the image to which raindrops are attached, respectively. In this example, the average frequency power 502 of the small block a500 is “120”, and the average frequency power 503 of the small block p501 is “80”.
As already described, an image with raindrops has a characteristic that the image frequency is low, and an image without raindrops has a characteristic that the image frequency is high. For this reason, in general, when the average frequency power of each frequency range of an image taken when raindrops are attached to the lens of the in-vehicle camera 111 is compared with that of an image taken when no raindrops are attached, a higher frequency The average frequency power in the range tends to decrease significantly. FIG. 5 (b) and FIG. 5 (c) show an example thereof.
FIG. 6 is an explanatory diagram illustrating an example of a threshold comparison method in a sample block of an image to which raindrops are not attached according to the first embodiment of the present invention.
The horizontal axis of the graph indicates the 16 small blocks 600 in the sample block, and the vertical axis indicates the average frequency power 601 (unit: dB). Each bar graph corresponding to the values a to p on the horizontal axis shows the values of the average frequency powers 502 to 503 from the small block a500 to the small block p501 in FIG. For example, the bar graph 602 corresponding to the value “a” on the horizontal axis indicates the value “130” of the average frequency power 502 of the small block a500 of the image to which no raindrop is attached. By comparing the average frequency power value of each small block with a predetermined frequency power threshold value FPTH 603, the presence or absence of raindrops is determined.
Here, the value of the frequency power threshold FPTH 603 is assumed to be 95. The example of FIG. 6 shows that the average frequency power of all small blocks exceeds the frequency power threshold FPTH 603.
FIG. 7 is an explanatory diagram illustrating an example of a threshold comparison method in a sample block of an image to which raindrops are attached according to the first embodiment of the present invention.
Similar to FIG. 6, the horizontal axis of the graph indicates 16 small blocks 700 in the sample block, and the vertical axis indicates the average frequency power 701 (unit: dB). Each bar graph corresponding to the values a to p on the horizontal axis shows values of average frequency powers 502 to 503 from the small block a500 to the small block p501 in FIG. For example, the bar graph 702 corresponding to the value a on the horizontal axis indicates the value “120” of the average frequency power 502 of the small block a500 of the image to which raindrops are attached. By comparing the average frequency power value of each small block with a predetermined frequency power threshold FPTH 703, the presence or absence of raindrops is determined. Here, the value of the frequency power threshold FPTH 703 is assumed to be 95. In the example of FIG. 7, the average frequency power of four small blocks a, b, e, and f exceeds the frequency power threshold FPTH 703, and the average frequency power of 12 small blocks other than a, b, e, and f is The frequency power threshold FPTH 703 or less is shown.
FIG. 8 is an explanatory diagram illustrating an example of the operation of the raindrop score calculation unit 103 according to the first embodiment of this invention.
First, the raindrop score calculation unit 103 counts the number of small blocks whose average frequency power is equal to or less than the frequency power threshold FPTH to obtain a total score. Next, the raindrop score calculation unit 103 compares this total score with the raindrop score threshold value of the sample block (FIG. 8A), and determines whether or not raindrops adhere to each sample block.
In the example of FIG. 8, a flag of “0” is placed in a small block whose average frequency power is equal to or higher than the frequency power threshold FPTH. A flag “1” is placed in a small block whose average frequency power is equal to or less than the frequency power threshold FPTH.
FIG. 8B is an example of the raindrop score of the sample block of the image with no raindrops shown in FIGS. 5B and 6. In the case of FIG. 8B, since the flag “1” does not exist, the total score is “0”, which is equal to or less than the raindrop score threshold “10”.
FIG. 8C is an example of the raindrop score of the sample block of the image to which the raindrops shown in FIG. 5C and FIG. 7 are attached. In the case of FIG. 8C, since there are twelve “1” flags, the total score is “12”, which is equal to or higher than the raindrop score threshold “10”, so that the corresponding sample block is determined to have raindrop adhesion. .
The method described with reference to FIGS. 5 to 8 is a method for determining the presence or absence of raindrops based on the estimation that the image frequency of a sample block including many small blocks having a small average frequency power is low. As a result, even if it is highly permeable dirt such as water droplets, adhesion to the lens can be accurately detected. However, this is the raindrop detection according to the present invention in which the presence or absence of raindrops is determined by utilizing the characteristic that the image frequency of an image with raindrops is lowered and the image frequency of an image without raindrops is increased. This is merely an example of the specific method, and various specific methods other than those described above may actually be included in the scope of the present invention.
For example, the image frequency analysis unit 102 may divide the sample block corresponding to the quadrant A in FIG. 4B into more than 16 small blocks or may be divided into fewer than 16 small blocks. However, it may not be divided into a plurality of small blocks. When the sample block is not divided into a plurality of small blocks, the image frequency analysis unit 102 calculates the average frequency power of the entire quadrant A, and determines whether or not the value exceeds the threshold value. It may be determined whether or not raindrops adhere to the surface.
However, the average frequency power calculated without dividing the sample block is easily affected by the DC component, but the DC component is not easily affected by the attachment of raindrops. For this reason, in order to accurately determine the presence or absence of raindrops, it is possible to divide the sample block into a plurality of small blocks within a range where the amount of calculation does not increase so much that the frequency power in the high frequency range can be observed. desirable.
FIG. 9 is an explanatory diagram illustrating an example of the operation of the raindrop area information generation unit 104 according to the first embodiment of this invention.
The raindrop area information generation unit 104 calculates the raindrop determination result for each sample block obtained by the raindrop score calculation unit 103 for each sample block obtained by the raindrop score calculation unit 103 for each divided region of the image shown in FIG. A flag “0” or “1” indicating the raindrop determination result is set. Specifically, the raindrop area information generation unit 104 puts a flag “0” on the divided area (900) determined not to have raindrops attached, and sets “0” to the divided area (901) determined to have raindrops attached. The raindrop area information is generated with the flag "1". By referring to the raindrop area information, it is possible to visually observe where the raindrops are attached on the screen.
FIG. 10 is an explanatory diagram illustrating an example of the operation of the raindrop determination unit 105 according to the first embodiment of this invention.
The raindrop area information makes it possible to visually know where the raindrops are attached in the image, but depending on the type of the peripheral recognition function, the raindrops are attached to each image (for example, the entire image 200 in FIG. 9). There may be a case where a binary output indicating the determination result of the absence of raindrops or adhesion of raindrops is required.
Therefore, the raindrop determination unit 105 arbitrarily sets a raindrop score threshold (FIG. 10A) for the entire image, and “1 (indicates that raindrops are attached)” in the raindrop region information generated by the raindrop region information generation unit 104. Compare with the total raindrop score, which is the sum of the entire images of the flags.
The raindrop determination result for each sample block of the image 200 in FIG. 10B is the same as that shown in FIG. In this example, since the total raindrop score is “32” and the raindrop score threshold is “30” or more, the raindrop determination unit 105 displays raindrop determination information indicating “image attached” as the raindrop determination result in this image. Output. If the total raindrop score is equal to or less than the raindrop score threshold, the raindrop determination unit 105 outputs raindrop determination information indicating “no image attached”. Accordingly, the peripheral state recognition unit 106 can know the presence or absence of raindrops for each image.
Next, a second embodiment of the present invention will be described. Except for the differences described below, the external recognition apparatus 100 of the second embodiment is the same as the external recognition apparatus 100 of the first embodiment, and therefore, illustration and description of portions other than the differences are omitted.
FIG. 11 is an explanatory diagram illustrating an example of the operation of the raindrop determination unit 105 according to the second embodiment of this invention.
Specifically, FIG. 11 does not output one raindrop determination result for one image (for example, the entire image 200 in FIG. 10B), but provides a plurality of image division regions to perform image division. It is an example which outputs a raindrop determination result for every area | region. The raindrop determination result for each sample block of the image 200 in FIG. 11B is the same as that shown in FIGS. 9 and 10B. As shown in FIG. 11B, the raindrop determination unit 105 arbitrarily sets the raindrop score threshold value (FIG. 11A) of the image segmentation region, and further sets one image 200 as A, B, C, Dividing into five areas 1100 of D and E, the total raindrop score is calculated for each of the divided areas.
FIG. 11B shows an example in which each divided region 1100 includes 16 (2 × 8) blocks. The raindrop determination unit 105 compares the raindrop score threshold of the image segmentation area with the total raindrop score for each segmentation area to obtain raindrop determination information for each image segmentation area. If the total raindrop score for each divided region is equal to or greater than the raindrop score threshold, the raindrop determination unit 105 outputs “1” indicating “image present” as the raindrop determination information for each divided region, and the total raindrop score for each divided region. Is equal to or less than the raindrop score threshold, “0” indicating “no image attachment” is output as raindrop determination information for each divided region.
As described above, by subdividing the image area where the raindrop determination is performed, it is possible to transmit to the subsequent stage dirt diagnosis unit 108 and the periphery recognition unit 107 which portion of the image has raindrop adhesion. Accordingly, it is possible to finely control the recognition operation of the peripheral state, such as instructing the stop of the recognition operation only in a part of the area where raindrops are attached.
Next, Embodiment 3 of the present invention will be described. Except for the differences described below, the external recognition apparatus 100 of the third embodiment is the same as the external recognition apparatus 100 of the first embodiment, and therefore, illustration and description of portions other than the differences are omitted. Moreover, since each part of the external field recognition apparatus 100 of Example 3 shown in figure has the same function as each part which attached | subjected the same referential mark of Example 1 except the difference demonstrated below, those description is demonstrated. Is omitted.
FIG. 12 is a functional block diagram illustrating an example of the configuration of the raindrop detection unit 101 according to the third embodiment of the present invention.
Here, in order to improve the analysis accuracy of the image frequency, an example in which the image frequency analysis unit is divided into two steps is shown. The raindrop detection unit 101 in FIG. 12 includes a first image frequency analysis unit 1200 that performs image frequency analysis for each region divided into large image sizes, and a first raindrop score that obtains a raindrop score for each region of the large image size. A raindrop score calculation unit 1201, a second image frequency analysis unit 1202 (same as the image frequency analysis unit 102 in FIG. 1) that performs image frequency analysis for each area divided into small image sizes, and the above-described small image size A second raindrop score calculation unit 1203 (same as the raindrop score calculation unit 103 in FIG. 1), a raindrop region information generation unit 1204 (same as the raindrop region information generation unit 104 in FIG. 1), and a raindrop score for each region; A raindrop determination unit 1205 (same as the raindrop determination unit 105 in FIG. 1).
The operations of the first image frequency analysis unit 1200 and the raindrop score calculation unit 1201 will be described below. In the example shown below, the “small image size” is the same as the size of the divided area shown in FIG. 2, and the “large image size” is larger than the “small image size”. The size is smaller than the size of the entire image of one frame. Therefore, the second image frequency analysis unit 1202 and the second raindrop score calculation unit 1203 perform the same operations as the image frequency analysis unit 102 and the raindrop score calculation unit 103 described in FIG. Furthermore, the raindrop area information generation unit 1204 and the raindrop determination unit 1205 perform the same operations as the raindrop area information generation unit 104 and the raindrop determination unit 105 described in FIG. In the following, description of each part that performs the same operation as that shown in FIG. 1 is omitted.
FIG. 13 is an explanatory diagram illustrating an example of the operation of the first image frequency analysis unit 1200 according to the third embodiment of this invention.
The first image frequency analysis unit 1200 sets an image size larger than the image size of the region division set by the second image frequency analysis unit 1202 when dividing the image 200 of the in-vehicle camera. For example, FIG. 13A shows an example in which the image size after region division of the first image frequency analysis unit 1200 is set to 64 × 64 pixels. FIG. 13B shows an example in which the second image frequency analysis unit 1202 sets the image size after region division to 32 × 32 pixels.
The method for obtaining the image frequency of the first image frequency analysis unit 1200 is the same as that already described except that the image size after the area division to be referred to is different, and the calculation method of the average frequency power is shown in FIGS. The description is omitted because it is the same as 5.
Note that the image size after region division of the first image frequency analysis unit 1200 may be arbitrarily set, but a size larger than the image size after region division of the second image frequency analysis unit 1202 is always set.
FIG. 14 is an explanatory diagram illustrating an example of the operation of the first raindrop score calculation unit 1201 in consideration of the peripheral area according to the third embodiment of the present invention.
The first raindrop score calculation unit 1201 obtains the average frequency power by the procedure described with reference to FIGS. 4, 5, and 13 (a), and then the average frequency power of each region and the average frequency power of the surrounding region. Are added at a predetermined ratio to obtain the final average frequency power Pave of each region. Here, the region for which the final average frequency power Pave is calculated is described as a region of interest. The peripheral area includes at least a plurality of areas adjacent to the attention area. In the following example, eight areas adjacent to the periphery of the attention area are set as the peripheral areas.
For example, the final average frequency power Pave is calculated by Equation 5.
ai = average frequency power obtained by dividing the region alone K1 = first coefficient (for example, 0.7)
K2 = second coefficient (eg, 0.375)
The first coefficient K1 and the second coefficient K2 are arbitrarily set according to the level of importance of the surrounding area, that is, the level indicating how much the average frequency power of the surrounding area is used for the raindrop detection together with the average frequency power of the attention area. May be set.
Depending on the position of the attention area, a part of the peripheral area may not exist (for example, the divided area f1305, the divided area d1303, and the divided area g1306 located in the periphery of the image). In that case, the Pave may be calculated by setting the average frequency power of the nonexistent portion to 0, or the Pave may be calculated by substituting the average frequency power of the region of interest as the average frequency power of the surrounding region. good. Or you may substitute the average frequency power approximated using the average frequency power of the adjacent division area by another method.
If the size of the divided area is large, it may not be possible to detect raindrops attached to a part of the attention area. However, for example, when it is determined that raindrops are attached to the peripheral area, it is highly likely that raindrops are also attached to the attention area. Therefore, as described above, not only the average frequency power of the region of interest but also the average frequency power of the surrounding region is used for raindrop detection of the region of interest by weighting calculation, so that even if the size of the divided region is large Therefore, it is possible to accurately determine the presence or absence of raindrops.
FIG. 15 is an explanatory diagram illustrating the thinning of the processing area by the first raindrop score calculation unit 1201 according to the third embodiment of this invention.
The first raindrop score calculation unit 1201 calculates the raindrop score by the same method as the method for obtaining the raindrop score of the sample block shown in FIG. However, since the image size of the divided area is different from the example of FIG. 8, the size of the sample block is different. As shown in the example of FIG. 13A, since the image size of one divided area is 64 × 64 pixels, the first raindrop score calculation unit 1201 calculates the number of image frequency data in quadrant A obtained by DFT. Is calculated as 32 × 32.
The first raindrop score calculation unit 1201 uses the number of image frequency data to count the number of small blocks whose average frequency power is equal to or lower than the frequency power threshold FPTH, and obtain a total score. Then, the first raindrop score calculation unit 1201 compares this total score with the raindrop score threshold of the sample block (FIG. 8A), and determines whether or not there is raindrop adhesion for each sample block.
In FIG. 15A, the first raindrop score calculation unit 1201 performs raindrop adhesion determination, and a flag “1” is set in an area that is provisionally determined to have raindrop adhesion, and an area that has been provisionally determined to have no raindrop adhesion. The result of placing the flag “0” is shown.
Subsequently, the processing of the second image frequency analysis unit 1202 and the second raindrop score calculation unit 1203 is performed. Here, the region of the flag “1” determined by the first raindrop score calculation unit 1201 as having raindrops attached Only the target is processed. FIG. 15B shows processing targets of the second image frequency analysis unit 1202 and the second raindrop score calculation unit 1203 according to the raindrop adhesion determination result of the first raindrop score calculation unit 1201 shown in FIG. An example of a portion 1503 (region tentatively determined to have raindrops attached) and a portion 1502 (region tentatively determined to have no raindrops attached) not to be processed is shown.
According to the above processing, the processing of the second image frequency analysis unit 1202 and the second raindrop score calculation unit 1203 is omitted for the area determined to have no raindrop adhesion by the first raindrop score calculation unit 1201. The amount of calculation can be reduced.
Next, a fourth embodiment of the present invention will be described. Except for the differences described below, the external recognition apparatus 100 of the fourth embodiment is the same as the external recognition apparatus 100 of the first embodiment, and therefore, illustration and description of portions other than the differences are omitted. Moreover, since each part of the external field recognition apparatus 100 of Example 4 shown in figure has the same function as each part which attached | subjected the same referential mark of Example 1 except the difference demonstrated below, those description is demonstrated. Is omitted.
FIG. 16 is a functional block diagram illustrating an example of the configuration of the raindrop detection unit 101 according to the fourth embodiment of the present invention.
As shown in FIG. 2A, in the image of the in-vehicle camera 111, the light shielding plate 201, the upper end 202 of the vehicle body (finisher), the bumper 203, and the license plate 204 are reflected as a fixed subject at all times. Yes. In addition to the above-described image frequency analysis method, by detecting that the fixed subject portion is distorted by raindrop adhesion by image processing, the accuracy of raindrop adhesion determination is improved.
As shown in FIG. 16, another raindrop detection unit 101 of the present invention includes an image frequency analysis unit 102 and a first raindrop score calculation unit 1602 (the raindrop score calculation unit of FIG. 1) similar to the raindrop detection unit 101 of FIG. 103), in addition to the raindrop area information generation unit 104 and the raindrop determination unit 105, an image distortion analysis unit 1600 that detects a distortion state of an image of a fixed subject portion due to raindrop adhesion by image processing, and an image distortion analysis A third raindrop score calculation unit 1601 that obtains a raindrop score addition value from the result.
FIG. 17 is an explanatory diagram illustrating an example of setting image observation points in the image distortion analysis unit 1600 according to the fourth embodiment of the present invention.
Image distortion detection determines the presence or absence of image distortion by obtaining a normalized SAD (Sum of Absolute Difference) of the image observation point and comparing the normalized SAD value with a predetermined threshold. FIG. 17 shows an example in which the image size of one image observation point is 8 × 8 pixels, and five image observation points 1700 to 1704 are set on the license plate at intervals.
The image size and the number of observation points of the image observation point may be set arbitrarily, but the observation point is always within the fixed subject part, that is, the range of the image where the same subject (especially the same edge part of the same subject) is always reflected. Set. Alternatively, the image observation point may be set in a range where a fixed subject portion other than the license plate, for example, the light shielding plate 201, the body upper end 202 (finisher) of the host vehicle, or the bumper 203 is reflected. Further, a fixed subject portion may be processed so that image distortion can be easily detected, such as pasting a specific pattern (for example, checkered pattern) on the shading plate 201, the vehicle body upper end 202 (finisher), and the bumper 203. good.
FIG. 18 is an explanatory diagram illustrating a detection processing method of the image distortion analysis unit 1600 according to the fourth embodiment of this invention.
Normalized SAD (NSAD) is calculated using Equation 6.
T is a template image, I is an observed image, T (i, j) is a pixel of the template image, I (i, j) is a pixel of the observed image, M is the number of pixels in the horizontal direction of the template image and the observed image, and N is The number of pixels in the horizontal direction of the observed image, i is the horizontal coordinate, and j is the vertical coordinate.
The graph of FIG. 18 shows an example of the result of obtaining normalized SAD 1800 for the period from time t1 to time t3 at one image observation point. When the correlation between the template image and the observed image is high, the normalized SAD value approaches 1.0 as much as possible. On the other hand, the normalized SAD value approaches 0 as the correlation between both images decreases.
If an image without raindrops is stored as a template image, an observed image with raindrop adhesion tends to cause image distortion, so the normalized SAD value shows a value close to “0”. On the other hand, since an observed image without raindrops hardly causes image distortion, the normalized SAD value shows a value close to “1”.
Here, when the raindrop adhesion determination threshold SADTH1801 is set to 0.8, an image at a time when the normalized SAD value is 0.8 or more is determined to be no raindrop adhesion and a raindrop score “0” is set. . The image at the time when the normalized SAD value is 0.8 or less is determined to have raindrop adhesion, and the raindrop score “1” is set.
In the example of FIG. 18, the period from time t1 to t2 is a period 1802 in which it is determined that no raindrops are attached, and the period from time t2 to t3 is a period 1803 in which it is determined that raindrops are attached.
FIG. 19 is an explanatory diagram illustrating an example of the operation of the third raindrop score calculation unit 1601 according to the fourth embodiment of this invention.
FIG. 19A shows a setting example of the normalized SAD score threshold value 1900 and the raindrop score addition value 1901. As the normalized SAD score threshold value 1900, a threshold value 1900 of the number of observation points for determining that there is image distortion (that is, raindrop adhesion) among the observation points set in FIG. In the raindrop score addition value 1901, an addition value to the total raindrop score obtained by the image frequency analysis unit of the raindrop determination unit 105 is set.
The total raindrop score 1902 of the normalized SAD observation points is the total value of the raindrop scores at all image observation points (five image observation points in the example of FIG. 17). In the example of FIG. 17, since five image observation points 1700 to 1704 are set, the value of the total raindrop score 1902 of the normalized SAD observation point at each time is any one from “0” to “5”.
In FIG. 19B, 8 images (that is, 8 frames) each taken at 8 times from time t1 to time t8 are input, and normalized SAD is obtained at 5 observation points of the images at each time. , And the total raindrop score 1902 of the normalized SAD observation points obtained as a result of the raindrop adhesion determination based thereon is tabulated. Here, from time t1 to time t4 when the total raindrop score 1902 of the normalized SAD observation point becomes smaller than the normalized SAD score threshold 1900, it is determined that there is no raindrop adhesion, and the raindrop score addition value 1903 is set to “0”. Is done. From time t5 to time t8 when the total raindrop score 1902 of the normalized SAD observation point becomes equal to or greater than the normalized SAD score threshold 1900, it is determined that raindrops are attached, and the raindrop score addition value 1903 is set to “5”.
FIG. 19 is an example, and arbitrary values may be set as the normalized SAD score threshold value 1900 and the raindrop score addition value 1901 .
FIG. 20 is an explanatory diagram illustrating an example of the operation of the raindrop determination unit 105 when image distortion analysis is performed in the fourth embodiment of the present invention.
When the raindrop detection unit 101 includes the image distortion analysis unit 1600 and the third raindrop score calculation unit 1601, the raindrop determination unit 105 adds the raindrop score obtained by the image distortion analysis process to the raindrop score 2000 obtained by the image frequency analysis process. Is added, and the presence / absence of raindrops is determined using this as the total raindrop score.
As described above, more accurate raindrop detection can be realized by determining the presence or absence of raindrops using the raindrop score based on image distortion analysis in addition to the raindrop score based on frequency analysis. Also, by using normalized SAD, brightness due to changes in the shooting environment (for example, whether shooting was performed in the daytime, at night, inside the tunnel, or outside the tunnel) Image distortion analysis that is less susceptible to the influence of (
Next, a fifth embodiment of the present invention will be described. Except for the differences described below, the external recognition apparatus 100 of the fifth embodiment is the same as the external recognition apparatus 100 of the first embodiment, and therefore, illustration and description of portions other than the differences are omitted. In addition, each part of the illustrated external environment recognition device 100 according to the fifth embodiment has the same functions as the respective parts denoted by the same reference numerals in the first to fourth embodiments, except for differences described below. These descriptions are omitted.
FIG. 21 is a functional block diagram illustrating an example of the external environment recognition apparatus 100 according to the fifth embodiment of the present invention.
Specifically, FIG. 21 shows an example of a configuration that ensures raindrop detection performance even when image contrast is lowered at night or the like by irradiating the lens surface of the in-vehicle camera 111 with infrared LED light.
When traveling at night or traveling through a long tunnel, the image contrast of the in-vehicle camera 111 is greatly reduced, and the accuracy of observation of raindrops by the image is also reduced. Therefore, in order to ensure raindrop detection performance even when the image contrast is reduced due to night driving or the like, the lens surface of the in-vehicle camera 111 is irradiated with infrared LED light to raise the raindrop contour, and image frequency analysis processing and Ensure raindrop detection performance of image distortion analysis processing. Here, infrared LED light refers to light in the near-infrared wavelength region in the vicinity of about 780 nm to 2600 nm, but is abbreviated as infrared LED in the following description. Infrared light affects the color of the color image, and the color of the affected image looks unnatural. Therefore, in the in-vehicle camera 111 and the digital camera, the wavelength component (about approximately) of visible light or more in internal signal processing. Filter processing for cutting (wavelength region of 780 nm or more) is performed.
However, in practice, it is difficult to completely remove a wavelength of 780 nm or more, and therefore the wavelength component of near-infrared light is sufficiently reflected in the image of the in-vehicle camera 111. The method of the present invention utilizes this characteristic. In addition, since the irradiation light of the infrared LED is invisible to the human eye, even if the blinking is repeated toward the in-vehicle camera 111, the driver or pedestrian of the other vehicle does not feel uncomfortable.
The control unit 112 controls turning on and off of the infrared LED (2100). The control unit 112 monitors the image of the in-vehicle camera 111 and shoots the in-vehicle camera 111 such as traveling at night or traveling in a long tunnel by a method such as checking whether the image contrast has greatly decreased for a long time. Determine the environment. If it is determined that the image contrast has greatly decreased, the control unit 112 instructs the infrared LED 2100 to turn on. When the image contrast is restored to the same state as in the daytime, the control unit 112 instructs the infrared LED 2100 to turn off. When the infrared LED 2100 is turned on to raise the raindrop contour, the infrared LED 2100 may be blinked at a predetermined period to confirm the raindrop contour.
Further, the process of checking whether or not the image contrast is significantly reduced may be performed by the external recognition apparatus 100 and a request for turning on and off the infrared LED may be transmitted from the external recognition apparatus 100 to the control unit 112.
When performing image frequency analysis processing and image distortion analysis processing by irradiating infrared LED light to raise the raindrop contour, the frequency power thresholds FPTH 603 and 703 of the image frequency analysis unit 102 shown in FIGS. You may change into the value suitable for conditions with low image contrast. Further, the raindrop adhesion determination threshold value SADTH1801 in the image distortion analysis unit 1600 shown in FIG. 18 may also be changed to a value suitable for a condition where the image contrast is low.
FIG. 22 is an explanatory diagram illustrating an example of an installation position of the infrared LED 2100 according to the fifth embodiment of the present invention.
One infrared LED 2100 may be installed so that the irradiation light hits the lens surface 2201 of the in-vehicle camera 111, or a plurality of infrared LEDs 2100 may be installed to secure an irradiation area.
FIG. 22A shows an example in which one infrared LED 2100 is installed on the in-vehicle camera 111. FIG. 22B is an example in which two infrared LEDs 2100 are installed above and below the in-vehicle camera 111 in order to ensure the area of the irradiated light. Furthermore, FIG.22 (c) is an example which installed four infrared LED2100 in the vehicle-mounted camera 111 above, the bottom, the left, and the right.
The number of infrared LEDs 2100 installed is arbitrary, and the installation position may be arbitrarily set in order to adjust the light distribution to the lens surface 2201 according to the ease of raindrop detection. Further, the infrared light source may be mounted inside the in-vehicle camera 111 instead of being a single LED device, and may be installed so as to irradiate irradiation light toward the lens surface 2201 from the inside of the lens.
Next, a sixth embodiment of the present invention will be described. Since each part of the external environment recognition apparatus 100 according to the sixth embodiment has the same functions as the parts denoted by the same reference numerals in the first to fifth embodiments except for the differences described below, their descriptions are as follows. Omitted.
FIG. 23 is a functional block diagram showing an example of the external environment recognition apparatus 100 according to the sixth embodiment of the present invention.
The external environment recognition apparatus 100 includes a raindrop detection unit 101, a surrounding state recognition unit 106, an alarm processing unit 110, and a driver notification unit 109. Furthermore, the external environment recognition apparatus 100 includes an infrared LED 2100, an in-vehicle camera 111, a memory 113, and a control unit 112. Each of the above parts is the same as each part having the same reference numerals described so far, except for the points described below.
The raindrop detection unit 101 is a block that predicts the presence or absence of raindrops on the in-vehicle camera lens surface from the image input from the in-vehicle camera 111. The raindrop detection unit 101 in FIG. 23 is equivalent to that shown in FIG. 16, and specifically, an image frequency for dividing an image input from the in-vehicle camera 111 into predetermined regions and obtaining an image frequency for each divided region. An analysis unit 102, a first raindrop score calculation unit 103 that calculates the reliability of raindrop adhesion as a score value for each divided area from the obtained image frequency, and image processing of a distortion state of an image of a fixed subject portion accompanying raindrop adhesion An image distortion analysis unit 1600 detected in step S3, a third raindrop score calculation unit 1601 that obtains a raindrop score addition value from the result of the image distortion analysis, and a raindrop region information generation unit that creates a raindrop score map for a region of one image 104, and a raindrop determination unit 105 that outputs the presence / absence of raindrops for each image from the raindrop score map and the third raindrop score as raindrop determination information.
The peripheral state recognition unit 106 recognizes the peripheral state of the vehicle with a dirt diagnosis unit 108 that diagnoses whether the lens surface of the in-vehicle camera 111 is dirty due to rain, snow, mud, cloudiness, or the like. And a warning processing unit 110 that transmits the recognition result information and the warning signal output from the surrounding recognition unit 107 to the host vehicle control unit 114. In addition, when the lens surface of the in-vehicle camera 111 is dirty due to rain, mud, cloudiness, etc., the dirt diagnosis unit 108 is in a state where accurate image recognition processing is difficult for the peripheral recognition unit 107 and erroneous recognition is likely to occur. Therefore, the FAIL signal is output to stop the operation of the peripheral recognition unit 107 temporarily or continuously.
Further, the dirt diagnosis unit 108 outputs to the driver notification unit 109 dirt notification information indicating that the periphery recognition unit 107 is temporarily or continuously stopped. The driver notification unit 109 recognizes the surroundings due to lens contamination by instructing at least one of the LED 2300, the speaker 2301, the display 2302, and the car navigation device 2303, which are separately provided outside, to speak, turn on, or display. The driver is notified of audio information or visual information indicating that the unit 107 is stopped. As a result, the driver knows that the periphery recognition unit 107 is stopped, and can perform a driving corresponding to the stop, or a treatment (for example, lens cleaning) for canceling the state.
The recognition process performed by the periphery recognition unit 107 in FIG. 23 may be similar to the recognition of the vehicle surrounding state performed by the periphery recognition unit 107 in FIG. Further, a lane departure warning process for issuing an alarm when the vehicle deviates from the lane while the host vehicle is traveling, and a blind spot alarm process for issuing an alarm when a person or other vehicle enters the blind spot from the driver of the host vehicle are also shown in FIG. It is included in the recognition processing performed by the peripheral recognition unit 107.
FIG. 24 is a flowchart illustrating an example of the operation of the external environment recognition device 100 according to the sixth embodiment of the present invention.
FIG. 24 shows an operation of the external environment recognition apparatus 100 shown in FIG. 23 as an example. When an image is first input from the in-vehicle camera 111 (S1), image frequency analysis processing and image distortion analysis processing operate in parallel using the same input image.
In the image frequency analysis process, the raindrop score threshold value (see FIG. 8A) of the sample block is set (S2). This threshold value may be input by the user, or may be determined by the raindrop detection unit 101 based on past detection results. Next, the image frequency analysis unit 102 calculates an image frequency for each divided region (S3). Subsequently, the first raindrop score calculation unit 103 calculates the raindrop score of each sample block (S4), and the raindrop region information generation unit 104 generates raindrop region information (S5). Next, after setting the raindrop score threshold TH for the entire image (S6), the raindrop determination unit 105 calculates the raindrop score SC1 for the image frequency analysis process (S7).
In the image distortion analysis process, the image distortion analysis unit 1600 calculates a normalized SAD for each observation point (S8). Subsequently, the third raindrop score calculation unit 1601 calculates the total score of the observation points based on the normalized SAD (S9), sets a threshold value of the normalized SAD score (S10), and performs the raindrop of the image distortion analysis process. A score addition value SC2 is determined (S11).
Next, the raindrop determination unit 105 adds the raindrop score SC1 of the image frequency analysis process and the raindrop score addition value SC2 of the image distortion analysis process to obtain a total score SC (S12). When the total score SC is equal to or greater than the raindrop score threshold TH of the entire image (S13), the raindrop determination unit 105 determines that raindrops are attached (S14). On the other hand, when the total score SC is equal to or less than the raindrop score threshold TH of the entire image (S14), the raindrop determination unit 105 determines that there is no raindrop adhesion (S17). The raindrop determination unit 105 transmits the determination result of the presence or absence of raindrop adhesion to the surrounding state recognition unit 106 as raindrop determination information.
The dirt diagnostic unit 108 that has received the determination result of the presence of raindrops sends a FAIL signal to the periphery recognition unit 107 to instruct operation stop (S15), and further displays that the operation of the periphery recognition unit 107 has stopped. The driver is notified (S16) by a method such as The dirt diagnostic unit 108 that has received the determination result of no raindrop adhesion instructs the peripheral recognition unit 107 to continue normal operation (S18).
As described above, FIG. 24 shows an example of the operation of the external environment recognition device 100 according to the sixth embodiment of the present invention. The operation of the external environment recognition device 100 according to the fourth embodiment is the same as that shown in FIG. It may be. The operation of the external environment recognition apparatus 100 according to the first and second embodiments may be the same as that illustrated in FIG. 24 except that S8 to S12 are omitted. In the operation of the external environment recognition apparatus 100 of the third embodiment, S8 to S12 are omitted, and the same processing as S2 to S4 is executed for the large-sized divided region shown in FIG. 24 may be the same as that shown in FIG. 24 except that S2 to S7 are executed for small-sized divided areas included in the divided areas determined to be attached.
The water droplets described so far are examples of lens stains, and in particular, are examples of lens stains with high light transmittance. Typical lens stains other than water droplets include mud, snow, white turbidity, etc., all of which are lens stains having lower light transmission than water droplets. As the lens dirt with lower light transmittance is obtained, the frequency power of the photographed image decreases more significantly. Therefore, the raindrop determination method by the raindrop determination unit 105 described above has lower light transmittance other than raindrops. It can also be applied to lens dirt.
According to the embodiments of the present invention described above, instead of a method of detecting the edge of the water droplet based on the pixel value of the image captured by the camera, the lens contamination is detected based on the frequency power of the image. Even such lens stains with high light transmittance can be detected with high accuracy.
In addition, this invention is not limited to above-described embodiment, Various modifications are included. Further, the above-described embodiment has been described in detail for easy understanding of the present invention, and is not necessarily limited to the one having all the configurations described. Further, a part of the configuration of an embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of an embodiment. In addition, it is possible to add, delete, and replace other configurations for a part of the configuration of each embodiment.
For example, the external environment recognition apparatus 100 may execute a combination of the two-stage lens contamination detection described as the third embodiment and the lens contamination detection based on the image distortion detection result described as the fourth embodiment. In addition, any one of the external environment recognition devices 100 according to the first to fifth embodiments may be connected to at least one of the LED 2300, the speaker 2301, the display 2302, the car navigation device 2303, and the like described in the sixth embodiment.
Each of the above-described configurations, functions, processing units, processing means, and the like may be realized by hardware by designing a part or all of them with, for example, an integrated circuit. Further, each of the above-described configurations, functions, and the like may be realized by software by interpreting and executing a program that realizes each function by the processor. Information such as programs, tables, and files for realizing each function is stored in a non-volatile semiconductor memory, a hard disk drive, a storage device such as an SSD (Solid State Drive), or a computer-readable non-readable information such as an IC card, an SD card, or a DVD. It can be stored on a temporary data storage medium.
DESCRIPTION OF SYMBOLS 100 External field recognition apparatus 101 Raindrop detection part 102 Image frequency analysis part 103 Raindrop score calculation part 104 Raindrop area | region information generation part 105 Raindrop determination part 106 Peripheral state recognition part 107 Perimeter recognition part 108 Dirt diagnosis part 109 Driver notification part 110 Alarm processing part 111 In-vehicle camera 112 Control unit 113 Memory
A lens dirt detection device for detecting camera lens dirt,
The lens dirt detection device includes a control unit and a memory connected to the control unit,
The memory holds pixel value data of an image taken by the camera,
The image is divided into a plurality of first areas, and a plurality of second areas each having a plurality of the first areas and smaller than the overall size of the image,
Based on the pixel value data, before calculating the image frequency power of the plurality of first regions, calculating the image frequency power of the plurality of second regions,
Based on the image frequency power of each second region, determine the presence or absence of the lens dirt,
Based on the pixel value data of each of the first regions included in each of the second regions determined to have the lens contamination, included in each of the second regions determined to have lens contamination. Calculating the image frequency power of multiple frequency ranges for each first region;
Determining whether the image frequency power of each of the plurality of frequency ranges of the first region exceeds a predetermined first threshold;
When the number of the frequency ranges in which the image frequency power does not exceed the first threshold exceeds a predetermined second threshold, it is determined that there is lens contamination of the camera in the first region;
A lens dirt detection device that outputs a determination result of the presence or absence of the lens dirt based on the image frequency power of each first region .
The lens dirt detection device according to claim 1,
The control unit is based on image frequency power obtained by weighting the image frequency power of each second region and the image frequency powers of the plurality of second regions adjacent to each second region. In addition, the lens dirt detection device is configured to determine the presence or absence of lens dirt in each of the second regions.
The lens dirt detection device according to claim 2,
Based on the pixel value data, the image of one or more fixed subject areas in which the same subject is always photographed included in the image is compared with the one or more pre-captured template images. Detects distortion in the fixed subject area
A lens dirt detection device that determines the presence or absence of the lens dirt based on the image frequency power of each of the first areas and the detected distortion of the image of the one or more fixed subject areas.
The lens dirt detection device according to claim 3,
Determining whether or not the sum of normalized difference absolute values between the plurality of fixed subject region images and the template image of the plurality of fixed subject regions exceeds a predetermined third threshold;
Based on the number of the first areas determined to have the lens dirt in the image and the number of the fixed subject areas where the normalized difference absolute value sum did not exceed the third threshold, A lens dirt detection device, wherein the presence or absence of lens dirt in the image is determined.
The control unit outputs a determination result of the presence or absence of the lens dirt for each of the first regions.
Determining whether the number of the first regions determined to have the lens dirt in the image exceeds a predetermined fourth threshold;
If the number of the first areas determined to have the lens dirt exceeds the fourth threshold, determine that the image has the lens dirt;
A lens dirt detection device that outputs a determination result of the presence or absence of the lens dirt of the image.
It is determined whether or not the number of the first areas determined to be contaminated in the third area smaller than the entire image and larger than the first area exceeds a predetermined fifth threshold. And
If the number of the first areas determined to have the lens dirt exceeds the fifth threshold, determine that the lens dirt is in the third area;
A lens dirt detection apparatus that outputs a determination result of the presence or absence of the lens dirt for each of the third regions.
A lens dirt detection device, further comprising one or more infrared light sources that irradiate infrared rays onto a lens surface of the camera when a shooting environment of the camera satisfies a predetermined condition.
At least one of a display device that outputs visual information indicating the determination result of the presence / absence of lens contamination and an audio output device that outputs audio information indicating the determination result of presence / absence of lens contamination is provided. Detection device.
A lens dirt detection method executed by a lens dirt detection device to detect lens dirt of a camera,
The lens dirt detection method includes:
The control unit calculates the image frequency power of the plurality of second regions before calculating the image frequency power of the plurality of first regions based on the pixel value data;
A procedure for the controller to determine the presence or absence of the lens stain based on the image frequency power of each second region;
Each of the second units determined to have the lens contamination based on the pixel value data of each of the first regions included in each of the second regions determined to have the lens contamination. Calculating image frequency power of a plurality of frequency ranges of each of the first regions included in a region;
A procedure for determining whether or not the image frequency power of each of the plurality of frequency ranges of the first region has exceeded a predetermined first threshold;
When the number of the frequency ranges in which the image frequency power does not exceed the first threshold exceeds a predetermined second threshold, the control unit determines that there is lens contamination of the camera in the first region;
And a step of outputting a determination result of the presence / absence of lens contamination based on the image frequency power of each of the first regions.
JP2013155552A 2013-07-26 2013-07-26 Lens dirt detection device and lens dirt detection method Active JP6245875B2 (en)
JP2013155552A JP6245875B2 (en) 2013-07-26 2013-07-26 Lens dirt detection device and lens dirt detection method
EP14828669.3A EP3026885A4 (en) 2013-07-26 2014-06-05 Dirty lens detection device and dirty lens detection method
US14/906,969 US9813593B2 (en) 2013-07-26 2014-06-05 Outside recognition apparatus and lens dirtiness detection method
PCT/JP2014/064986 WO2015011998A1 (en) 2013-07-26 2014-06-05 Dirty lens detection device and dirty lens detection method
JP2015026987A JP2015026987A (en) 2015-02-05
JP6245875B2 true JP6245875B2 (en) 2017-12-13
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JP2013155552A Active JP6245875B2 (en) 2013-07-26 2013-07-26 Lens dirt detection device and lens dirt detection method
US (1) US9813593B2 (en)
EP (1) EP3026885A4 (en)
JP (1) JP6245875B2 (en)
WO (1) WO2015011998A1 (en)
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