Patent Publication Number: US-11393128-B2

Title: Adhered substance detection apparatus

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The invention relates to an adhered substance detection apparatus and an adhered substance detection method. 
     Description of the Background Art 
     Conventionally, there has been known an adhered substance detection apparatus that detects an adhered substance adhered to a lens of a camera based on a captured image captured by the camera mounted on a vehicle or the like. The adhered substance detection apparatus detects the adhered substance, for example, based on a difference between time-series captured images. 
     However, as for a conventional technology, accuracy in detecting an adhered substance further needs to be improved. 
     SUMMARY OF THE INVENTION 
     According to one aspect of the invention, an adhered substance detection apparatus includes a calculator and a determiner. The calculator calculates an edge feature for each cell based on edge vectors of pixels within the cell. The cell consists of a predetermined number of the pixels in a captured image. The calculator further calculates a region feature for each unit region based on the calculated edge features of the cells within the unit region. The unit region is a predetermined region and consists of a predetermined number of the cells. The determiner determines an adherence state of an adhered substance on a lens of a camera based on the region feature. The calculator calculates, as the region feature, number of the cells having an edge strength of zero. The edge strength is a part of the edge feature. When the number of the cells having the zero edge strength is equal to or greater than a predetermined number in a predetermined attention area in the captured image, the determiner determines not to perform a determination for detecting whether or not the lens of the camera is in an entirely-covered state in which the lens of the camera is entirely covered by the adhered substance. 
     An object of the invention is to provide an adhered substance detection apparatus and an adhered substance detection method capable of improving accuracy in detecting an adhered substance. 
     These and other objects, features, aspects and advantages of the invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1A  illustrates an outline of an adhered substance detection method of the embodiment; 
         FIG. 1B  illustrates an outline of the adhered substance detection method of the embodiment; 
         FIG. 1C  illustrates an outline of the adhered substance detection method of the embodiment; 
         FIG. 2  is a block diagram of the adhered substance detection apparatus of the embodiment; 
         FIG. 3  illustrates a process of a calculator; 
         FIG. 4  illustrates the process of the calculator; 
         FIG. 5  illustrates the process of the calculator; 
         FIG. 6  illustrates the process of the calculator; 
         FIG. 7  illustrates a process of a calculator in a modification; 
         FIG. 8  illustrates the process of the calculator in the modification; 
         FIG. 9A  illustrates a process of a determiner; 
         FIG. 9B  illustrates the process of the determiner; 
         FIG. 9C  illustrates the process of the determiner; 
         FIG. 9D  illustrates the process of the determiner; and 
         FIG. 10  is a flowchart illustrating a procedure of the process that is performed by the adhered substance detection apparatus of the embodiment. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     Hereinafter, an adhered substance detection apparatus and an adhered substance detection method of an embodiment will be described in detail with reference to the drawings. The invention is not limited to the embodiment described below. 
     First, with reference to  FIGS. 1A to 1C , an outline of the adhered substance detection method of the embodiment will be described.  FIGS. 1A to 1C  illustrate the outline of the adhered substance detection method of the embodiment. 
     As shown in  FIG. 1A , for example, a captured image I is captured by a vehicle-mounted camera in a state in which snow is on a surface of a lens of the vehicle-mounted camera. As an example, below will be described a case in which an adhered substance detection apparatus  1  (see  FIG. 2 ) using the adhered substance detection method of the embodiment detects a state in which the surface of the lens of the vehicle-mounted camera is entirely covered by snow (hereinafter referred to as “entirely-covered state”). The adhered substance detection apparatus  1  detects the entirely-covered state based on a feature (hereinafter referred to also as “edge feature”) relating to a luminance gradient of each pixel of the captured image I. 
     More specifically, as shown in  FIG. 1A , the adhered substance detection apparatus  1  calculates the edge features of pixels PX within a ROI (Region of Interest) that is a predetermined attention area in the captured image I and detects an adherence state of snow based on the calculated edge features of the pixels PX. The edge feature includes an angle feature and a strength feature. The angle feature is defined as a direction of an edge vector (luminance gradient) (hereinafter referred to also as “edge direction”) of each pixel PX. The strength feature is defined as a size of the edge vector (hereinafter referred to also as “edge strength”) of each pixel PX. 
     In order to reduce processing load in image processing, the adhered substance detection apparatus  1  uses the edge feature of a cell  100  that is a group of a predetermined number of the pixels PX. Thus, the processing load in the image processing can be reduced. The ROI consists of one or more unit regions UA each of which is a group of the cells  100 . 
     Next, the adhered substance detection apparatus  1  calculates a region feature for each of the unit regions UA, based on the edge features extracted from the cells  100 . In other word, the region feature is a statistical edge feature for each unit region UA. For example, the region feature includes number of pair regions and a sum of edge strengths of the pair regions. Here, “pair region” is defined as a pair of the cells  100  that are adjacent to each other and that have the edge directions opposite to each other. The adhered substance detection apparatus  1  performs an entirely-covered state determination based on the region feature. 
     More specifically, as shown in  FIG. 1A , a predetermined number of the cells  100  are arranged in a vertical direction and in a horizontal direction in each of the unit regions UA in the captured image I. The adhered substance detection apparatus  1  first calculates the edge features of the cells  100  for each of the unit regions UA (a step S 1 ). The edge feature means the edge direction and the edge strength, as described above. 
     An edge direction for each of the cells  100  is, as shown in  FIG. 1A , a representative value for the directions of the edge vectors of the pixels PX within each of the cells  100 . The edge direction of each of the cells  100  is identified as one of angle groups that have a predetermined angle range. In an example shown in  FIG. 1A , the edge direction for each of the cells  100  is identified as one of up, down, left and right directions each of which has a 90-degree angle range. An edge strength for each of the cells  100  is a representative value for the strengths of the edge vectors of the pixels PX within each of the cells  100 . A calculation process of the edge feature for each cell  100  will be described later in detail with reference to  FIGS. 3 and 4 . 
     Next, the adhered substance detection apparatus  1  calculates the region feature for each of the unit regions UA based on the edge features of the cells  100 , calculated in the step S 1 , within each of the unit regions UA. Here, number of the pair regions described above and a sum of the edge strengths of the pair regions are calculated (a step S 2 ). 
     Then, the adhered substance detection apparatus  1  performs the entirely-covered state determination based on the calculated number of the pair regions and the calculated sum of the edge strengths of the pair regions (a step S 3 ). Here, generally, many pair regions tend to be located on a boundary (edge) of background of the captured image I or the pair regions on the edge of the background of the captured image I tend to have a strong feature. 
     Therefore, when the feature is equal to or smaller than a predetermined value, i.e., when both the number of the pair regions and the sum of the edge strengths of the pair regions are small, as shown in  FIG. 1A , the adhered substance detection apparatus  1  determines that the edge of the background is unrecognizable (unclear) so that the surface of the lens of the vehicle-mounted camera is entirely covered. 
     One of similar states to the entirely-covered state is a dark situation, as shown in an upper drawing of  FIG. 1B , such as night with no light nearby so that the background of the captured image I is unrecognizable (unclear) due to darkness (referred to also as “dark case No. 1”), but is not the entirely-covered state. In such a case, the edge in the background of the captured image I is unrecognizable. In other words, both number of the pair regions and a sum of the edge strengths of the pair regions are small. Thus, there is a possibility that the adhered substance detection apparatus  1  incorrectly determines the case as the entirely-covered state in the foregoing entirely-covered state determination. 
     Here, a lower drawing of  FIG. 1B  is an image showing the angle groups visualized in the captured image I (hereinafter referred to as “visualized angle-group image”). A white region extends in a large area in the visualized angle-group image. 
     The white region represents one or more cells having no angle (i.e., zero edge strength). The term “zero edge strength” does not have to mean an absolute zero angle, and may include a state in which an edge strength is within a predetermined range that is set, for example, in designing. It is known that the cells having no angle (hereinafter referred to as “no-angle cell(s)”) generally account for approximately less than 10% of an image in a case other than the dark case No. 1. Thus, the adhered substance detection method of this embodiment utilizes the characteristic of the dark case No. 1 that can be acquired from the visualized angle-group image, to prevent an erroneous determination in detecting the entirely-covered state. 
     More specifically, as shown in  FIG. 1C , when number of the no-angle cells in the ROI is equal to or greater than a predetermined number, the adhered substance detection method of the embodiment, determines not to perform the entirely-covered state determination because it is difficult to determine the entirely-covered state due to darkness. 
     Thus, according to the adhered substance detection method of the embodiment, it is possible to incorrectly determine such a state as the dark case No. 1, as the entirely-covered state. In other words, accuracy in detecting an adhered substance can be improved. 
     In the adhered substance detection method of this embodiment, a process of determining the entirely-covered state is performed for each frame of the captured image I. The process derives a determination result indicative of whether or not the processed frame is determined as the entirely-covered state (e.g. “1” or “−1”). However, as shown in  FIG. 1C , when the entirely-covered state determination is not performed, the process derives a determination result other than “1” and “−1,” for example, “0 (zero)” for the frame. 
     A similar state to the entirely-covered state is not limited to the dark case No. 1 shown in  FIG. 1B . Another case similar state to the entirely-covered state, except the dark case No. 1, will be described later with reference to  FIGS. 9A to 9D . 
     The below will be more specifically described a configuration example of the adhered substance detection apparatus  1  utilizing the foregoing adhered substance detection method of the embodiment. 
       FIG. 2  is a block diagram of the adhered substance detection apparatus  1  of the embodiment.  FIG. 2  illustrates the block diagram only including configuration elements necessary to explain a feature of this embodiment, and general configuration elements are omitted. 
     In other words, the configuration elements illustrated in  FIG. 2  are functional concept, and are not necessarily physically configured as illustrated in  FIG. 2 . For example, a concrete configuration, such as separation and integration of functional blocks, is not limited to the configuration illustrated in  FIG. 2 . Some or all functional blocks of the adhered substance detection apparatus  1  may be functionally or physically separated or integrated into an appropriate unit, according to a load or a use situation of the functional blocks. 
     As illustrated in  FIG. 2 , the adhered substance detection apparatus  1  of the embodiment includes a memory  2  and a controller  3 . The adhered substance detection apparatus  1  is connected to a camera  10  and various devices  50 . 
     In  FIG. 2 , the adhered substance detection apparatus  1  is configured separately from the camera  10  and the various devices  50 , but is not limited thereto. The adhered substance detection apparatus  1  may be configured as one unit with at least one of the camera  10  and the various devices  50 . 
     The camera  10  is, for example, a vehicle-mounted camera including a lens, such as a fish-eye lens, and an imaging element, such as a CCD (Charge Coupled Device) and a CMOS (Complementary Metal Oxide Semiconductor). The camera  10  is, for example, provided at each position capable of capturing front, rear and side images of a vehicle, and outputs the captured image I to the adhered substance detection apparatus  1 . 
     The various devices  50  acquire a detection result from the adhered substance detection apparatus  1  to perform various control of the vehicle. Some among the various devices  50  are a display that informs a user of an adhered substance on the lens of the camera  10  and instructs the user to remove the adhered substance from the lens, a removing device that removes the adhered substance from the lens by ejecting a fluid, air, etc. onto the lens, and a vehicle controller that controls autonomous driving. 
     The memory  2  is, for example, a semiconductor memory device, such as a RAM (Random Access Memory) and a flash memory, or a memory device, such as a hard disk or an optical disk. In an example shown in  FIG. 2 , the memory  2  stores group information  21  and threshold information  22 . 
     The group information  21  is information relating to the foregoing angle groups. For example, the group information  21  includes the predetermined angle range for the angle groups, and the like. The threshold information  22  is information relating to a threshold that is used for a determination process that is performed by a determiner  33  described later. The threshold information  22  includes, for example, the predetermined number (threshold) for the no-angle cells shown in  FIG. 1C , and the like. 
     The controller  3  is a CPU (Central Processing Unit) or an MPU (Micro Processing Unit), etc. that executes various programs stored in a memory in the adhered substance detection apparatus  1 , using a RAM as a work area. The controller  3  may be an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). 
     The controller  3  includes an acquisition part  31 , a calculator  32  and the determiner  33 . The controller  3  executes an information processing function and/or produces an effect described below. 
     The acquisition part  31  acquires the captured image I captured by the camera  10 . The acquisition part  31  performs grayscale processing that converts luminance of pixels of the acquired captured image I into gray levels from white to black based on the luminance of the pixels of the captured image I, and also performs a smoothing process for the pixels of the captured image I. Then, the acquisition part  31  outputs the captured image I to the calculator  32 . An arbitrary smoothing filter, such as an averaging filter and Gaussian filter may be used for the smoothing process. Further, the grayscale processing and/or the smoothing process may be omitted. 
     The calculator  32  calculates the edge feature for each of the cells  100  in the captured image I received from the acquisition part  31 . Here will be described, with reference to  FIGS. 3 and 4 , the calculation process of the edge feature that is performed by the calculator  32 . 
       FIGS. 3 and 4  illustrate the calculation process that is performed by the calculator  32 . As shown in  FIG. 3 , the calculator  32  first performs an edge detection process for each pixel PX to detect a strength of an edge ex in an X-axis direction (the horizontal direction of the captured image I) and a strength of an edge ey in a Y-axis direction (the vertical direction of the captured image I). For the edge detection process, an arbitrary edge detection filter, such as Sobel filter and Prewitt filter, may be used. 
     Next, the calculator  32  calculates an edge vector V based on the detected strengths of the edge ex and the edge ey in the X-axis direction and the Y-axis direction, using a trigonometric function. The calculator  32  calculates the edge direction that is an angle θ between the edge vector V and an X axis, and the edge strength that is a length L of the edge vector V. 
     Next, the calculator  32  calculates a representative edge direction value for each of the cells  100  based on the calculated edge vectors V of the pixels PX within each cell  100 . More specifically, as shown in an upper drawing of  FIG. 4 , the calculator  32  categorizes the edge directions of the edge vectors V of the pixels PX within each cell  100  into four angle groups (0) to (3) in up, down, left and right directions. The four angle groups (0) to (3) are generated by dividing an edge direction range from −180° to 180° into the four up, down, left and right directions to have the 90-degree angle range each. 
     More specifically, among the edge directions of the edge vectors V of the pixels PX, an edge direction within an angle range from −45° to 45° is categorized as the angle group (0) by the calculator  32 ; an edge direction within an angle range from 45° to 135° is categorized as the angle group (1) by the calculator  32 ; an edge direction within an angle range from 135° to 180° or −180° to −135° is categorized as the angle group (2) by the calculator  32 ; and an edge direction within an angle range from −135° to −45° is categorized as the angle group (3) by the calculator  32 . 
     Then, as shown in a lower drawing of  FIG. 4 , for each cell  100 , the calculator  32  creates a histogram having bins of the angle groups (0) to (3). Then, when, among frequencies of the bins, a largest frequency is equal to or greater than a predetermined threshold THa, the calculator  32  derives the angle group corresponding to the bin having the largest frequency (in a case of  FIG. 4 , the angle group (1)), as the representative edge direction value for the cell  100 . 
     A frequency of the histogram is calculated by summing the edge strengths of the pixels PX categorized into a same angle group, among the pixels PX within the cell  100 . As a more specific example, when three pixels PX having the edge strengths 10, 20, and 30 respectively, are categorized in the angle group (0), a bin of the histogram, the frequency of the angle group (0) is 60 that is calculated by adding 10, 20 and 30 of the edge strengths. 
     Based on the histogram calculated in such a manner, the calculator  32  calculates a representative edge strength value for each cell  100 . More specifically, when a frequency of the bin having the largest frequency in the histogram is equal to or greater than the predetermined threshold THa, the representative edge strength value of the cell  100  is the frequency of the bin having the largest frequency. In other words, a calculation process of the representative edge strength value performed by the calculator  32  is a calculation process that calculates a feature relating to the edge strengths corresponding to the representative edge direction value, within each cell  100 . 
     Meanwhile, when the frequency of the bin having the largest frequency is smaller than the predetermined threshold THa, the calculator  32  regards the edge direction for the cell  100  as “invalid,” in other words, “no representative edge direction value,” i.e., “no angle” described above. Thus, when variation of the edge directions of the pixels PX is large, calculating an edge direction as a representative value is prevented. 
     The process in  FIGS. 3 and 4  performed by the calculator  32  is only an example, and if a representative edge direction value can be calculated, any process may be performed. For example, the adhered substance detection apparatus  1  may calculate an average value of the edge directions of the pixels PX within each cell  100 , and may identify one of the angle groups (0) to (3) corresponding to the average value as the representative edge direction value. 
     For example, in  FIG. 4 , the cell  100  is an area of 16 pixels PX that are arranged in a 4×4 matrix. However, number of the pixels PX in one cell  100  may be arbitrarily set. Further, number of the pixels PX arranged in the vertical direction may be different from number of the pixels PX arranged in the horizontal direction, such as a 3×5 matrix. 
     The calculator  32  in  FIG. 2  calculates the region feature for each of the unit regions UA based on the calculated edge features of the cells  100 . 
     More specifically, the calculator  32  calculates number of pair regions  200  and a sum of edge strengths of the pair regions  200  for each of the unit regions UA. 
     Here will be described, with reference to  FIGS. 5 and 6 , a calculation process, performed by the calculator  32 , for the number of the pair regions  200  and the sum of the edge strengths for the pair regions  200 .  FIGS. 5 and 6  illustrate the calculation process that is performed by the calculator  32 . 
       FIG. 5  illustrates a case in which two pair regions  200  share no cell  100 , and  FIG. 6  illustrates a case in which two pair regions  200  share one cell  100 . 
     As shown in  FIG. 5 , the calculator  32  horizontally and vertically scans a plurality of the cells  100  arranged in the unit region UA in the horizontal direction and the vertical direction, to detect the pair region  200 . In other words, the calculator  32  extracts, as the pair region  200 , a pair of the cells  100  within the unit region UA that are adjacent to each other and that have the edge directions opposite to each other. 
     Then, the calculator  32  calculates the number of the extracted pair regions  200  and the sum of the edge strengths of the extracted pair regions  200 . For example, when the extracted two pair regions  200  share no cell  100 , as shown in  FIG. 5 , the calculator  32  determines that the calculated number of the extracted pair regions  200  is two and calculates a sum of the edge strengths of the four cells  100  included in the two pair regions  200  as the sum of the edge strengths of the extracted pair regions  200 . 
     For example, when the extracted two pair regions  200  share one cell  100 , as shown in  FIG. 6 , the calculator  32  determines that the calculated number of the extracted pair regions  200  is two and calculates a sum of the edge strengths of the three cells  100  included in the two pair regions  200  as the sum of the edge strengths of the extracted pair regions  200 . 
     The calculator  32  may calculate two or more representative edge direction values for each of the cells  100  based on a plurality of types of the angle groups, not only the foregoing angle groups of the “four up, down, left and right directions” but also, for example, angle groups of “four oblique directions,” to calculate the region feature. This will be described with reference to  FIGS. 7 and 8 .  FIGS. 7 and 8  illustrate a process performed by the calculator  32  of a modification. 
     The calculator  32  calculates a first representative edge direction value based on the “four up, down, left and right directions” that are first angle groups. The calculator  32  also calculates a second representative edge direction value based on the “four oblique directions” that are second angle groups, as shown in  FIG. 7 . 
     In this case, the calculator  32  categorizes the edge directions of the edge vectors V of the pixels PX within each cell  100  into four angle groups (4) to (7) that are generated by dividing the angle range from −180° to 180° into the four oblique directions to have a 90-degree angle range each. 
     More specifically, among the edge directions of the edge vectors V of the pixels PX, an edge direction within an angle range from 0° to 90° is categorized as the angle group (4) by the calculator  32 ; an edge direction within an angle range from 90° to 180° is categorized as the angle group (5) by the calculator  32 ; an edge direction within an angle range from −180° to −90° is categorized as the angle group (6) by the calculator  32 ; and an edge direction within an angle range from −90° to 0° is categorized as the angle group (7) by the calculator  32 . 
     As shown in the lower drawing of  FIG. 4 , for each cell  100 , the calculator  32  creates a histogram having bins of the angle groups (4) to (7). When, among frequencies of the bins of the generated histogram, a largest frequency is equal to or greater than the predetermined threshold THa, the calculator  32  derives the angle group corresponding to the bin having the largest frequency, as the second representative edge direction value for the cell  100 . 
     Thus, as shown in  FIG. 8 , the two representative edge direction values are calculated for each cell  100 . Then, as shown in  FIG. 8 , the calculator  32  extracts, as the pair region  200 , a pair of the cells  100  that are adjacent to each other and one of which has at least one of the first and second representative edge direction values opposite to one of the first and second representative edge direction values of the other. 
     In other words, since the calculator  32  calculates the first representative edge direction value and the second representative edge direction value for each cell  100 , the calculator  32  can extract the pair region  200  that cannot be extracted when the calculator  32  only calculates one representative edge direction value for each cell  100 . 
     For example, when the edge directions of the pixels PX are categorized into the first angle groups, an edge direction 140° is not opposite to an edge direction −40°. However, when the edge directions of the pixels PX are categorized into the second angle groups, those two edge directions are opposite to each other. Therefore, a change of the edge directions within each cell  100  can be detected more accurately. 
     With reference back to  FIG. 2 , the calculator  32  further calculates, as the region feature, number of the cells  100  having the edge strength, a part of the edge feature, of zero. In other words, the calculator  32  calculates number of the no-angle cells described above, an average luminance value, etc. Then the calculator  32  outputs, to the determiner  33 , the calculated region feature for each unit region UA. 
     The determiner  33  performs the entirely-covered state determination based on the region feature calculated by the calculator  32 , i.e., the number of the pair regions  200  and the sum of the edge strengths in the pair regions  200 . 
     However, as shown in  FIG. 1C , when the number of the no-angle cells is equal to or greater than the predetermined number in the ROI, i.e., when the image satisfies a condition of the dark case No. 1, the determiner  33  determines not to perform the entirely-covered state determination because it is difficult to determine the entirely-covered state due to darkness. 
     When a state is not the dark case No. 1 but is similar to the entirely-covered state, the determiner  33  may perform a process to prevent an incorrect determination.  FIGS. 9A to 9D  illustrate the process performed by the determiner  33 . The dark case No. 1 is already described above so that another case will be described with reference to  FIGS. 9A to 9D . 
     First, a dark case No. 2 will be described with reference to  FIGS. 9A to 9B . As shown in an upper drawing of  FIG. 9A , the dark case No. 2 is a case of insufficient illumination that causes the background of the captured image I to be unclear; however, a dark region in the captured image I in the dark case No. 2 is not large as compared to the dark case No. 1, because, for example, surroundings of the vehicle are slightly lit by a brake light and the like of the vehicle. 
     Here, a lower drawing of  FIG. 9A  illustrates a visualized angle-group image of the captured image I in the dark case No. 2. In the visualized angle-group image, the white (no-angle) cells do not extend in a large area in the captured image I as compared to the dark case No. 1. However, the no-angle cells are scattered in the dark region of the captured image I. 
     The determiner  33  uses the feature of the dark case No. 2 that is acquired from the visualized angle-group image to prevent an incorrect determination in the entirely-covered state determination. 
     More specifically, as shown in  FIG. 9B , when the unit regions UA satisfying a condition below account for a predetermined percentage or greater in the ROI, the determiner  33  determines not to perform the entirely-covered state determination because it is difficult to determine the entirely-covered state due to darkness, like the dark case No. 1. The condition is that the unit regions UA i) include a predetermined number or a greater number of the no-angle cells and ii) have an average luminance value lower than a predetermined value. In other words, when the unit regions UA that i) include the predetermined number or a greater number of the no-angle cells and ii) have an average luminance value lower than the predetermined value, account for the predetermined percentage or greater in the ROI, the determiner  33  determines not to perform the entirely-covered state determination. 
     Thus, it is possible to prevent from incorrectly determining such a state as the dark case No. 2, as the entirely-covered state. In other words, accuracy in detecting an adhered substance can be improved. 
     Being similar to the dark case No. 1, in the dark case No. 2, the determiner  33  does not perform the entirely-covered state so that the determiner  33  derives “0” as the determination result. 
     Next, a dark case No. 3 will be described with reference to  FIGS. 9C to 9D . As shown in an upper drawing of  FIG. 9C , the dark case No. 3 is a case of a blurred edge of the background of the captured image I because, for example, the captured image I was captured by a rear camera at night, having snow melting agent or the like on the lens that causes the captured image I to be entirely blurred and a portion of a bumper of the vehicle in the captured image I to be whited out by a light of the vehicle. 
     When the lens is entirely covered by snow, an upper portion of an image may be bright, for example, by lights in a tunnel. However, when the lens is entirely covered by snow, an image of which a lower portion is bright is seldom captured because such an image is captured, for example, when the lens is lit from a road surface. 
     The determiner  33  uses the feature of the dark case No. 3 to prevent an incorrect determination in the entirely-covered state determination based on a difference in average luminance value between an ROI_U and an ROI_D that are defined in an upper portion and a lower portion of an image, respectively. 
     A lower drawing of  FIG. 9C  is a correlation chart having a Y-axis representing the average luminance value of the upper portion ROI_U of the captured image I and an X-axis representing the average luminance value of the lower portion ROI_D of the captured image I. 
     According to the correlation chart, when such a state as the dark case No. 3 is incorrectly determined as the entirely-covered state, luminance of the upper portion and luminance of the lower portion in the image are disproportionate so that the difference in luminance between the upper portion and the lower portion is concentrated and has a tendency that the luminance of the lower portion ROI_D is greater than the luminance of the upper portion ROI_U by 50 or greater (i.e., the luminance is located below a dotted line in the chart). 
     Therefore, as shown in  FIG. 9D , when the average luminance value of the lower portion ROI_D is greater than the average luminance value of the upper portion ROI_U by a predetermined value or greater (50 or greater in the example shown in  FIG. 9C ), the determiner  33  determines that the lens of the camera  10  is not in the entirely-covered state. In other words, the determiner  33  derives a determination result “−1.” 
     Thus, it is possible to prevent from incorrectly determining such a state as the dark case No. 3, as the entirely-covered state. In other words, accuracy in detecting an adhered substance can be improved. 
     With reference back to  FIG. 2 , the determiner  33  outputs the determination result of the determination to the various devices  50 . 
     Next will be described is a procedure of the process that is performed by the adhered substance detection apparatus  1  of the embodiment, with reference to  FIG. 10 .  FIG. 10  is a flowchart illustrating the procedure of the process that is performed by the adhered substance detection apparatus  1  of the embodiment.  FIG. 10  illustrates the procedure of the process that is performed for each frame of the captured image I. 
     As shown in  FIG. 10 , first the acquisition part  31  acquires the captured image I (a step S 101 ). The acquisition part  31  performs the grayscale processing and the smoothing process for the captured image I. 
     Next, the calculator  32  calculates the edge feature for each of the cells  100  of the captured image I (a step S 102 ). Further, the calculator  32  calculates the region feature for each of the unit regions UA based on the calculated edge features of the cells (a step S 103 ). 
     Then, the determiner  33  determines, based on the region feature calculated by the calculator  32 , whether or not the captured image I satisfies the condition of the dark case No. 1 (a step S 104 ). When the captured image I satisfies the condition of the dark case No. 1 (Yes in the step S 104 ), the determiner  33  derives, for example, “0 (zero)” as the determination result and moves to a step S 109  without performing the entirely-covered state determination. 
     When the captured image I does not satisfy the condition of the dark case No. 1 (No in the step S 104 ), the determiner  33  next determines whether or not the captured image I satisfies the condition of the dark case No. 2 (a step S 105 ). When the captured image I satisfies the condition of the dark case No. 2 (Yes in the step S 105 ), the determiner  33  derives, for example, “0 (zero)” as the determination result and moves to the step S 109  without performing the entirely-covered state determination. 
     When the captured image I does not satisfy the condition of the dark case No. 2 (No in the step S 105 ), the determiner  33  determines whether or not the captured image I satisfies the condition of the dark case No. 3 (a step S 106 ). When the captured image I satisfies the condition of the dark case No. 3 (Yes in the step S 106 ), the determiner  33  determines that the lens of the camera  10  is not in the entirely-covered state (a step S 107 ). Then, the determiner  33  derives, for example, “−1” as the determination result. 
     When the captured image I does not satisfy the condition of the dark case No. 3 (No in the step S 106 ), the determiner  33  performs the entirely-covered state determination based on the number of the pair regions and the sum of the edge strengths of the pair regions calculated by the calculator  32  (a step S 108 ). 
     Then, the determiner  33  outputs the determination result to the various devices  50  (the step S 109 ) and ends the process. 
     As described above, the adhered substance detection apparatus  1  of the embodiment includes the calculator  32  and the determiner  33 . Each of the cell  100  consists of the predetermined number of the pixels PX in the captured image I. Based on the edge vectors of the predetermined number of the pixels PX within each of the cells  100 , the calculator  32  calculates the edge feature for each of the cells  100 . Further, each of the unit regions UA, a predetermined region, consists of the predetermined number of the cells  100 . Based on the calculated edge features of the cells  100  within each of the unit regions UA, the calculator  32  calculates the region feature for each of the unit regions UA. The determiner  33  determines, based on the region feature, a state of an adhered substance on the lens of the camera  10 . The calculator  32  calculates, as the region feature, number of the cells  100  having the edge strength, a part of the edge feature, of zero. Further, when the number of the cells  100  having the zero edge strength (no angle) is equal to or greater than the predetermined number in the ROI in the captured image I, the determiner  33  determines not to perform the entirely-covered state determination for detecting whether or not the lens of the camera  10  is in the entirely-covered state. 
     Thus, accuracy in detecting an adhered substance can be improved by the adhered substance detection apparatus  1  of the embodiment. Especially, in a case of the foregoing dark case No. 1, an incorrect determination can be prevented. 
     Further, the calculator  32  calculates the average luminance value as the region feature. When the unit regions UA that i) include the predetermined number or a greater number of the no-angle cells and ii) have an average luminance value lower than the predetermined value, account for the predetermined percentage or greater in the ROI, the determiner  33  determines not to perform the entirely-covered state determination. 
     Thus, accuracy in detecting an adhered substance can be improved by the adhered substance detection apparatus  1  of the embodiment. Especially, an incorrect determination can be prevented in the foregoing dark case No. 2. 
     Further, when the average luminance value of the ROI_D defined in the lower portion of the captured image I is greater than the average luminance value of the ROI_U defined in the upper portion of the captured image I by a predetermined value or greater, the determiner  33  determines that the lens of the camera  10  is not in the entirely-covered state. 
     Thus, accuracy in detecting an adhered substance can be improved by the adhered substance detection apparatus  1  of the embodiment. Especially, an incorrect determination can be prevented in the foregoing dark case No. 3. 
     The foregoing embodiment describes an example in which the calculator  32  categorizes the edge directions into the four angle groups that is generated by dividing the edge direction range from −180° to 180° into the four directions, to have a 90-degree angle range each. However, the angle range that the angle groups have is not limited to 90°. The edge direction range from −180° to 180° may be divided, for example, into six angle groups to have a 60-degree angle range each. 
     Further, the angle range for the first angle groups may be different from the angle range for the second angle groups. For example, the first angle groups may have a 90-degree angle range, and the second angle groups may have a 60-degree angle range. Further, lines dividing the second angle groups are displaced by 45° from lines dividing the first angle groups. However, the displaced angle may be greater or smaller than 45°. 
     The captured image I in the foregoing embodiment is, for example, an image captured by a vehicle-mounted camera. However, the captured image I may be an image captured by, for example, a security camera, a camera installed on a street light or the like The captured image I may be any image captured by a camera having a lens on which a substance may be adhered. 
     Further effects and modifications may be derived easily by a person skilled in the art. Thus, broader modes of the present invention may not be limited to the description and typical embodiment described above. 
     While the invention has been shown and described in detail, the foregoing description is in all aspects illustrative and not restrictive. It is therefore understood that numerous other modifications and variations can be devised without departing from the scope of the invention that is defined by the attached claims and equivalents thereof.