Patent Document

TECHNICAL FIELD 
       [0001]    The present invention relates to a surrounding environment recognition device that recognizes a surrounding environment on the basis of an image captured by a camera. 
       BACKGROUND ART 
       [0002]    Recently, there has been a tendency for an increase in the number of applications for recognizing a surrounding environment from an image captured by a camera installed in a vehicle. Among these, there is known a technology for determining whether a camera lens normally recognizes an object when a camera is installed outside a vehicle interior (PTL 1). In the technology of PTL 1, when foreign matter adhering to a camera lens is detected and a ratio of the region exceeds a threshold value, an application for recognizing a surrounding environment is stopped and an application stop state is notified to a user. From the past, there are also known various technologies that detect an immovable region not changing against a moving background from an image obtained in a vehicle travel state and detect an object only by a region excluding the immovable region. 
       CITATION LIST 
     Patent Literature 
       [0003]    PTL 1: JP 2012-38048 A 
       SUMMARY OF INVENTION 
     Technical Problem 
       [0004]    However, a method of suggesting a change in recognition object recognizing range to the user is not mentioned. As in the related art, there is concern that a surrounding environment is carelessly detected when the user overestimates the application only by an operation in which the application is stopped or the object is detected only by a region excluding the immovable region in the lens stain state. 
         [0005]    The invention is made in view of the above-described circumstances and an object thereof is to provide a surrounding environment recognition device that suggests a sensing enabled range changing in response to a lens stain state to a user. 
       Solution to Problem 
       [0006]    A surrounding environment recognition device for solving the problem is a surrounding environment recognition device that recognizes a surrounding environment on the basis of an external environment image captured by a camera, and the surrounding environment recognition device includes: an image acquisition unit that acquires the image; an application execution unit that executes an application for recognizing a recognition object from the image; a lens state diagnosis unit that diagnoses a lens state of the camera on the basis of the image; a sensing range determination unit that determines a sensing enabled range capable of sensing the recognition object and a sensing disabled range incapable of sensing the recognition object on the basis of the lens state diagnosed by the lens state diagnosis unit when the application is executed; and a notification control unit that notifies at least one of the sensing enabled range and the sensing disabled range of the sensing range determination unit. 
       Advantageous Effects of Invention 
       [0007]    According to the invention, since deterioration in performance of an image recognition application caused by a stain of a lens is notified to a user, it is possible to allow a driver to drive a vehicle while paying attention to a surrounding environment in a lens stain state without an overestimation for a camera recognition function. Further, objects, configurations, and advantages other than those described above are proved by the description of the embodiment below. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0008]      FIG. 1  is a block diagram showing an internal configuration of a surrounding environment recognition device. 
           [0009]      FIG. 2  is a block diagram showing an internal function of a lens state diagnosis unit. 
           [0010]      FIG. 3  is a block diagram showing an internal function of a sensing range determination unit. 
           [0011]      FIG. 4  is a block diagram showing an internal function of an application execution unit. 
           [0012]      FIG. 5  is a block diagram showing an internal function of a notification control unit. 
           [0013]      FIG. 6  is a schematic diagram showing an entire configuration of an in-vehicle camera system. 
           [0014]      FIG. 7  is a diagram showing an example of a screen displayed on an in-vehicle monitor. 
           [0015]      FIG. 8  is a diagram showing an example of a screen displayed on the in-vehicle monitor. 
           [0016]      FIG. 9  is a diagram showing an example of an image displayed on a front glass of a vehicle. 
           [0017]      FIGS. 10( a ) to 10( c )  are diagrams showing a method of detecting a particulate deposit adhering to a lens. 
           [0018]      FIGS. 11( a ) and 11( b )  are diagrams showing a method of detecting sharpness of a lens. 
           [0019]      FIGS. 12( a ) to 12( c )  are diagrams showing a method of detecting a water droplet adhering to a lens. 
           [0020]      FIGS. 13-1 ( a ) and  13 - 1 ( b ) are diagrams showing a method of determining a pedestrian sensing enabled range in response to a size of a particulate deposit. 
           [0021]      FIGS. 13-2 ( a ) and  13 - 2 ( b ) are diagrams showing an example of an image in a pedestrian sensing enabled state and a pedestrian sensing disabled state. 
           [0022]      FIGS. 13-3 ( a ) and  13 - 3 ( b ) are diagrams showing an example of a pedestrian sensing enabled range. 
           [0023]      FIGS. 14-1 ( a ) and  14 - 1 ( b ) are diagrams showing a method of determining a vehicle sensing enabled range in response to a size of a particulate deposit. 
           [0024]      FIGS. 14-2 ( a ) and  14 - 2 ( b ) are diagrams showing an example of an image in a vehicle sensing disabled state and a vehicle sensing enabled state. 
           [0025]      FIGS. 15( a ) and 15( b )  are diagrams showing a method of determining a barrier sensing enabled range in response to a size of a particulate deposit. 
           [0026]      FIG. 16  is a diagram showing a definition for a durable shielding ratio and a standard size of a recognition object of each application. 
           [0027]      FIGS. 17( a ) and 17( b )  are diagrams showing a method of determining a sensing enabled range in response to sharpness. 
           [0028]      FIGS. 18( a ) and 18( b )  are diagrams showing a definition for a maximal detection distance set in response to sharpness of each application. 
           [0029]      FIGS. 19( a ) and 19( b )  are diagrams showing a method of determining a sensing enabled range in response to a size of a water droplet. 
           [0030]      FIGS. 20( a ) and 20( b )  are diagrams showing a definition for a maximal detection distance and a limited water droplet occupying ratio set in response to a water droplet adhering state in each application. 
           [0031]      FIG. 21  is a diagram comparing a sensing enabled range in response to a recognition object. 
       
    
    
     DESCRIPTION OF EMBODIMENTS 
       [0032]    Next, an embodiment of a surrounding environment recognition device of the invention will be described below with reference to the drawings. Further, in the embodiment below, an example will be described in which the surrounding environment recognition device of the invention is applied to an in-vehicle environment recognition device mounted on a vehicle such as an automobile, but the invention is not limited to the in-vehicle environment recognition device. For example, the surrounding environment recognition device can be also applied to a construction machine, a robot, a monitoring camera, an agricultural machine, and the like. 
         [0033]      FIG. 1  is a block diagram showing an internal function of the surrounding environment recognition device. 
         [0034]    An in-vehicle surrounding environment recognition device  10  of the embodiment is used to recognize a surrounding environment of a vehicle on the basis of an image obtained by capturing an external environment by an in-vehicle camera. The surrounding environment recognition device  10  includes an in-vehicle camera which captures an outside image of the vehicle and a recognition device which recognizes a surrounding environment on the basis of an image captured by the in-vehicle camera. However, the in-vehicle camera is not essentially necessary for the surrounding environment recognition device as long as only an outside image captured by the in-vehicle camera or the like can be acquired. 
         [0035]    The surrounding environment recognition device  10  includes, as illustrated in  FIG. 1 , an image capturing unit  100 , a lens state diagnosis unit  200 , a sensing range determination unit  300 , an application execution unit  400 , and a notification control unit  500 . 
         [0036]    The image capturing unit  100  captures a vehicle surrounding image acquired by, for example, in-vehicle cameras  101  (see  FIG. 6 ) attached to front, rear, left, and right sides of a vehicle body (an image acquisition unit). The application execution unit  400  recognizes an object from the image acquired by the image capturing unit  100  and executes various applications for detecting a pedestrian or a vehicle (hereinafter, referred to as an application). 
         [0037]    The lens state diagnosis unit  200  diagnoses a lens state of each in-vehicle camera  101  on the basis of the image acquired by the image capturing unit  100 . The in-vehicle camera  101  includes an imaging element such as a CMOS and a lens of an optical system disposed at the front side of the imaging element. Further, the lens of the embodiment is not limited to a focus adjusting lens and generally also includes a glass of an optical system (for example, a stain preventing filter lens or a polarizing lens) disposed at the front side of the imaging element. 
         [0038]    The lens state diagnosis unit  200  diagnoses a stain caused by a particulate deposit, cloudness, or a water droplet of the lens. When the in-vehicle camera  101  is disposed, for example, outside the vehicle, there is concern that a particulate deposit of mud, trash, or bugs may adhere to the lens or the lens may become cloudy like obscure glass due to dust or a water stain. Further, there is concern that the water droplet adheres to the lens so that the lens becomes dirty. When the lens of the in-vehicle camera  101  becomes dirty, a part or the entirety of a background captured in an image is hidden or a background image becomes dim due to low sharpness or becomes distorted. As a result, there is concern that the object may not be easily recognized. 
         [0039]    The sensing range determination unit  300  determines a sensing enabled range capable of recognizing a recognition object on the basis of the lens state diagnosed by the lens state diagnosis unit  200 . The sensing enabled range changes in response to a stain degree including a particulate deposit adhering position and a particulate deposit size with respect to the lens. Also, the sensing enabled range also changes in response to the application executed by application execution unit  400 . For example, even when the lens stain degree and the distance from the lens to the object are the same, the sensing enabled range becomes wider when the recognition object of the application is a large object such as a vehicle compared to a small object such as a pedestrian. 
         [0040]    The notification control unit  500  executes a control that notifies at least one of the sensing enabled range and the sensing disabled range to a user on the basis of information from the sensing range determination unit  300 . The notification control unit  500  notifies a change in sensing enabled range to the user, for example, in such a manner that the sensing enabled range is displayed or a warning sound or a message is generated for the user by the use of an in-vehicle monitor or a warning device. In this way, the information can be provided for the vehicle control device in response to the sensing enabled range so that the vehicle control device can use the information for a vehicle control. 
         [0041]      FIG. 6  is a schematic diagram showing an example of a system configuration of the vehicle and an entire configuration of the in-vehicle camera system. The surrounding environment recognition device  10  has an internal configuration of the image processing device  2  that executes an image process of the in-vehicle camera  101  and an internal function of the vehicle control device  3  that executes a vehicle control or a notification to a driver on the basis of a process result transmitted from the image processing device. The image processing device  2  includes, for example, the lens state diagnosis unit  200 , the sensing range determination unit  300 , and the application execution unit  400  and the vehicle control device  3  includes the notification control unit  500 . 
         [0042]    The vehicle  1  includes a plurality of in-vehicle cameras  101 , for example, four in-vehicle cameras  101  including a front camera  101   a  capturing a front image of the vehicle  1 , a rear camera  101   b  capturing a rear image thereof, a left camera  101   c  capturing a left image thereof, and a right camera  101   d  capturing a right image thereof. Accordingly, the peripheral image of the vehicle  1  can be continuously captured. In addition, the in-vehicle camera  101  may not be provided at a plurality of positions, but may be provided at one position. Further, only the front or rear image maybe captured instead of the peripheral image. 
         [0043]    The left and right in-vehicle cameras  101  may be configured as cameras attached to side mirrors or cameras installed instead of the side mirrors. The notification control unit  500  is a user interface and is mounted on hardware different from the image processing device  2 . The notification control unit  500  executes a control that realizes a preventive safety function or a convenience function by the use of a result obtained by the application execution unit  400 . 
         [0044]      FIG. 7  is a diagram showing an example of a screen displayed on the in-vehicle monitor. 
         [0045]    From the past, there is known an overview display method of suggesting a sensing enabled range of an application obtained when a predetermined application is executed during a normal operation of the system while a distance space is viewed from the upside of an own vehicle (the vehicle  1 ) to the in-vehicle monitor  700 . 
         [0046]    A minimum sensing line  701  in which an object closest to the vehicle  1  can be sensed (recognized) by a predetermined application is indicated by a small oval surrounding the periphery of the vehicle  1  and a maximum sensing line  702  in which an object farthest from the vehicle  1  can be sensed (recognized) by the same application is indicated by a large oval. When a space between the minimum sensing line  701  and the maximum sensing line  702  becomes a sensing range  704  and the lens is in a normal state without a stain, the entire sensing range  704  becomes the sensing enabled range. In addition, a reference numeral  703  indicated by the dashed line in the drawing indicates a part in which the image capturing ranges of the adjacent in-vehicle cameras overlap each other. 
         [0047]    The sensing range  704  is set in response to the application in execution. For example, when the object of the application is relatively large like the vehicle  1 , the maximum sensing line  702  and the minimum sensing line  701  respectively increase in size. Further, when the object is relatively small like a pedestrian or the like, the maximum sensing line  702  and the minimum sensing line  701  respectively decrease in size. 
         [0048]    When a stain or the like exists on the lens of the in-vehicle camera  101 , it is difficult to detect a recognition object in a background part hidden by the stain or the like even within the sensing range  704 . As a result, there is concern for a performance deterioration state in which the application cannot exhibit predetermined performance. In the surrounding environment recognition device of the invention, a control of notifying the performance deterioration state of the application to the user is executed. 
         [0049]    As a notification method, for example, a method can be employed in which the sensing enabled range and the sensing disabled range of the sensing range  704  are visually displayed on the in-vehicle monitor or the like so that the performance deterioration state is accurately notified to the user. In this display method, a detectable distance from the vehicle  1  can be easily checked and a sensing ability deterioration degree caused by deterioration in performance can be easily suggested to the user. Further, the performance deterioration state of the application may be notified to the user in such a manner that an LED provided on a meter panel or the like inside a vehicle interior is turned on or a warning sound or a vibration is generated. 
         [0050]      FIG. 8  is a diagram showing an example of a screen displayed on the in-vehicle monitor. An in-vehicle monitor  801  displays an image  802  captured by the in-vehicle camera  101  installed at the front part of the vehicle and also displays a sensing enabled region  803  and a sensing disabled region  804  to be displayed to overlap the image  802 . The image  802  includes a road R at the front side of the vehicle  1  and left and right white lines WL indicating a travel vehicle lane. By such a display, the sensing enabled region  803  set in response to the lens state can be notified to the driver while the lens state of the in-vehicle camera  101  (see  FIG. 6 ) is viewed. Then, since the sensing enabled region  803  and the lens state indicating, for example, a message that “wiping is necessary since a far place is not visible in such a stain degree” are viewed simultaneously, the sensing ability of the in-vehicle camera  101  can be easily notified to the driver. 
         [0051]      FIG. 9  is a diagram showing an example of an image displayed on a front glass of the vehicle. 
         [0052]    Here, a scene which is viewed from the vehicle interior through a front glass  901  by the use of a head up display (HUD) overlaps a real world. Since the sensing enabled region  803  or the sensing disabled region  804  are viewed while overlapping a road of the real world, the sensing enabled region or the sensing distance of the actual in-vehicle camera  101  can be easily and visually checked. Here, since a projection type head up display for the front glass  901  shields a driver&#39;s view, a display on the entire face of the front glass  901  is difficult. For this reason, as illustrated in  FIG. 9  the overlap display with the road using the lower side of the front glass  901  may be performed in such a manner that the sensing enabled region  803  is suggested to overlap the real world by the use of the overlap display at the upper side of the front glass  901 . 
         [0053]    Next, the execution content of the lens state diagnosis unit  200 , the sensing range determination unit  300 , the application execution unit  400 , and the notification control unit  500  illustrated in  FIG. 1  will be described sequentially. 
         [0054]      FIG. 2  is a block diagram showing an internal function of the lens state diagnosis unit  200 . The lens state diagnosis unit  200  includes a particulate deposit detector  210 , a sharpness detector  220 , and a water droplet detector  230  and diagnoses a stain state in accordance with the type of stain adhering to the lens of the in-vehicle camera  101  on the basis of the image acquired by the image capturing unit  100 . 
         [0055]      FIGS. 10( a ) to 10( c )  are diagrams showing a method of detecting a particulate deposit adhering to the lens. Here,  FIG. 10( a )  shows an image  1001  at the front side of the in-vehicle camera  101  and  FIGS. 10( b ) and 10( c )  show a method of detecting the particulate deposit. 
         [0056]    As illustrated in  FIG. 10( a ) , the image  1001  is dirty since a plurality of particulate deposits  1002  adhere to the lens. The particulate deposit detector  210  detects the particulate deposit adhering to the lens, for example, the particulate deposit  1002  such as mud shielding the appearance of the background. When the particulate deposit  1002  such as mud adheres to the lens, the background is not easily visible and the brightness is continuously low compared to the periphery. Thus, it is possible to detect the particulate deposit  1002  by detecting a region having a small brightness change amount. 
         [0057]    First, the particulate deposit detector  210  divides an image region of the image  1001  into a plurality of blocks A (x, y) as illustrated in  FIG. 10( b ) . Next, the brightness values of the pixels of the image  1001  are detected and a total sum I t  (x, y) of the brightness values of the pixels included in the block A (x, y) is calculated for each block A (x, y). Then, a difference ΔI (x, y) between the total sum I t  (x, y) calculated for a captured image of a current frame and a total sum I t-1  (x, y) calculated for a captured image of a previous frame is calculated for each block A (x, y). Then, the block A (x, y) in which the difference ΔI (x, v) is smaller than those of the peripheral blocks is detected and a score SA (x, y) corresponding to the block A (x, y) is increased by a predetermined value, for example, “1”. 
         [0058]    The particulate deposit detector  210  calculates an elapse time tA from the initialization of the score SA (x, y) of each block A (x, y) after the above-described determination for all pixels of the image  1001 . Then, a time average SA (x, y)/tA of the score SA (x, y) is calculated in such a manner that the score SA (x, y) of each block A (x, y) is divided by the elapse time tA. The particulate deposit detector  210  calculates a total sum of the time average SA (x, y)/tA of all blocks A (x, y) and divides the total sum by the number of all blocks of the captured image to calculate a score average SA_ave. 
         [0059]    When a stain  1002  such as mud continuously adheres to the lens of the in-vehicle camera  101 , the score average SA_ave increases in each of the sequentially captured frames. In other words, when the score average SA_ave is large, there is a high possibility that mud or the like adheres to the lens for a long period of time. It is determined whether the time average SA (x, y)/tA exceeds a predetermined threshold value. Here, a region in which the time average exceeds the threshold value is determined as a region (a particulate deposit region) in which a background is not visible due to mud. This region is used to calculate the sensing enabled range of each application in response to the size of the region in which the time average exceeds the threshold value. Further, a final determination is made for the operation of each application by the use of the score average SA_ave.  FIG. 10( c )  shows a score example in which all blocks are depicted as color gradation depending on the score. Then, when the score is equal to or larger than a predetermined threshold value, a region  1012  is determined in which the background is not visible due to the particulate deposit. 
         [0060]    Next, an operation of the sharpness detector  220  will be described with reference to  FIGS. 11( a ) and 11( b ) .  FIGS. 11( a ) and 11( b )  are diagrams showing a method of detecting the sharpness of the lens. The sharpness detector  220  detects the lens state on the basis of a sharpness index representing whether the lens is clear or unclear. A state where the lens is not clear indicates, for example, a state where a lens surface becomes cloudy due to the stain and a contrast becomes low. Accordingly, an outline of an object is dimmed and the degree is indicated by the sharpness. 
         [0061]    As illustrated in  FIG. 11( a ) , the sharpness detector  220  sets a left upper detection region BG_L (Background Left), an upper detection region BG_T (Background Top), and a right upper detection region BG_R (Background Right) at a position where a horizontal line is reflected on the image  1001 . The upper detection region BG_T is set to a position including a horizontal line and a vanishing point where two lane marks WL are provided in parallel on the road intersect each other at a far position. The left upper detection region BG_L is set to the left side of the upper detection region BG_T and the right upper detection region BG_R is set to the right side of the upper detection region BG_T. The regions including the horizontal line are set so that edges are essentially included on the image. Further, the sharpness detector sets a left lower detection region RD_L (Road Left) and a right lower detection region RD_R (Road Right) at a position where the lane mark WL is reflected on the image  1001 . 
         [0062]    The sharpness detector  220  executes an edge detection process on pixels within each region of the left upper detection region BG_L, the upper detection region BG_T, the right upper detection region BG_R, the left lower detection region RD_L, and the right lower detection region RD_R. In the edge detection for the left upper detection region BG_L, the upper detection region BG_T, and the right upper detection region BG_R, an edge such as a horizontal line is essentially detected. Further, in the edge detection for the left lower detection region RD_L and the right lower detection region RD_R, the edge of the lane mark WL or the like is detected. 
         [0063]    The sharpness detector  220  calculates an edge strength value for each pixel included in the detection regions BG_L, BG_T, BG_R, RD_L, and RD_R. Then, the sharpness detector  220  calculates an average value Blave of the edge strength values of each of the detection regions BG_L, BG_T, BG_R, RD_L, and RD_R and determines a sharpness degree on the basis of the average value Blave. As illustrated in  FIG. 11( b ) , the sharpness is set so that the lens is clear as the edge strength becomes strong and the lens nclear as the edge strength becomes weak. 
         [0064]    It is determined that the application recognition performance is influenced when the calculated average value Blave is lower than standard sharpness. Then, the application performance deterioration degree is determined for each application by the use of the sharpness average value for each region. When the sharpness is lower than minimal sharpness α2, it is determined that the recognition in each application is difficult. 
         [0065]      FIGS. 12( a ) to 12( c )  are diagrams showing a method of detecting a water droplet adhering to the lens. 
         [0066]    The water droplet detector  230  of  FIG. 2  extracts a water droplet feature amount by comparing the brightness of the peripheral pixels on an imaging screen illustrated in  FIG. 12( a ) . The water droplet detector  230  sets pixels which are separated from an interest point by a predetermined distance (for example, three pixels) in the up direction, the right up direction, the right down direction, the left up direction, and the left down direction as inner reference points Pi and sets pixels which are further separated therefrom by a predetermined distance (for example, pixels more than three pixels) in the five directions as outer reference points Po. Next, the water droplet detector  230  compares the brightness for each inner reference point Pi and each outer reference point Po. 
         [0067]    There is a high possibility that the vicinity of the inside of the edge of the water droplet  1202  is brighter than the outside due to a lens effect. Here, the water droplet detector  230  determines whether the brightness of the inner reference point Pi at the inside of the edge of the water droplet  1202  is higher than the brightness of the outer reference point Po in each of five directions. In other words, the water droplet detector  230  determines whether the interest point is at the center of the water droplet  1202 . When the brightness of the inner reference point Pi in each direction is higher than the brightness of the outer reference point Po in the same direction, the water droplet detector  230  increases a score SB (x, y) of a region B (x, y) included in the interest point in  FIG. 12( b )  by a predetermined value, for example, “1”. As for the score of B (x, y), an instantaneous value at a predetermined time tB is stored and a past score stored for the time tB or more is discarded. 
         [0068]    The water droplet detector  230  executes the above-described determination for all pixels in a captured image. Then, the water droplet detector obtains a total sum of the score SB (x, y) of each block B (x, y) for an elapse time tB, calculates a time average score SB (x, y) by dividing the total sum by the time Tb, and calculates a score average SB_ave by dividing the time average score by the number of all blocks in the captured image. A degree in which the score SB (x, y) of each divided region exceeds a specific threshold value ThrB is determined as a score. Then, the divided region exceeding the threshold value and the score are depicted on a map as illustrated in  FIG. 12( c )  and a sum SB 2  of the scores on the map is calculated. 
         [0069]    When the water droplet continuously adheres to the lens of the in-vehicle camera  101 , the score average SB_ave for each frame increases. In other words, when the score average SB_ave is large, there is a high possibility that the water droplet adheres to the lens position. The water droplet detector  230  determines whether many water droplets adhere to the lens by the use of the score average SB_ave. The sum SB 2  is appropriate when the water droplet adhering amount on the lens is large and a failure determination on the entire system is made by the use of this value. In the determination of each logic, a separate water droplet occupying ratio is used to determine a maximal detection distance. 
         [0070]    Both the water droplet adhering amount and the score average SB_ave are used in the determination for deterioration in performance of the recognition application due to the stain of the lens. The a method of calculating the sensing enabled range is considered.  FIG. 12( c )  shows a score example in which all blocks are depicted as color gradation depending on the score. Then, when the score is equal to or larger than a predetermined threshold value, a region in which a background is not visible due to the water droplet is determined. 
         [0071]      FIG. 3  is a diagram showing an internal function of the sensing range determination unit. The sensing range determination unit  300  includes a particulate deposit distance calculation unit  310 , a sharpness distance calculation unit  320 , and a water droplet distance calculation unit  330  and executes a process of determining the sensing enabled range by the use of a diagnosis result of the lens state diagnosis unit  200 . In the particulate deposit distance calculation unit  310 , a sensing enabled range capable of guaranteeing the detection of each application by the use of the detection result of the particulate deposit detector  210  is converted. In the sharpness distance calculation unit  320 , a sensing enabled range capable of guaranteeing the detection of each application by the use of the detection result of the sharpness detector  220  is converted. In the water droplet distance calculation unit  330 , a sensing enabled range capable of guaranteeing the detection of each application by the use of the detection result of the water droplet detector  230  is converted. 
         [0072]    The particulate deposit distance calculation unit  310  calculates the sensing enabled range in response to the detection result of the particulate deposit detector  210 . It is determined whether the time average SA (x, y)/tA exceeds a predetermined threshold value by the use of the result of the particulate deposit detector  210 . Then, a region exceeding the threshold value is determined as a region in which a background is not visible due to mud. For example, as illustrated in  FIG. 13-1 ( a ), when a particulate deposit  1302  such as mud adheres to a left upper side of an image  1301 , it is determined that the time average SA (x, y)/tA corresponding to the region of the particulate deposit  1302  exceeds a predetermined threshold value. Accordingly, as indicated by a dark region  1303  in  FIG. 13-1 ( b ), a region in which a background is not visible due to the particulate deposit  1302  is selected on the image. 
         [0073]    Next, the sensing enabled range in this case is defined for each application. An important point herein is that the size of the recognition object in each application is different. First, an example for a pedestrian detection application will be described. 
         [0074]    &lt;Pedestrian Detection&gt; 
         [0075]    As illustrated in  FIGS. 13-2 ( a ) and  13 - 2 ( b ), a pedestrian P overlaps a region in which a background is not visible due to the particulate deposit  1302 . On the image, the size of the pedestrian P becomes different in response to a distance in the depth direction. Since a percentage (a ratio) in which the particulate deposit  1302  shields the pedestrian P increases as the pedestrian P is located at a far position, it is difficult to guarantee a detection at a far position and a detection in the left direction of the front fish-eye camera. In the example illustrated in  FIG. 13-2 ( a ), a pedestrian is separated from an own vehicle by 6.0 m and most part of the Pedestrian is hidden by the shade of the particulate deposit  1302  so that only a shape smaller than 40% of the size of the pedestrian is visible. For this reason, the pedestrian detector  430  of the application execution unit  400  cannot recognize the pedestrian (an unrecognizable state). Meanwhile, as illustrated in  FIG. 13-2 ( b ) when the pedestrian is separated from the own vehicle by 1.0 m, a shape equal to or larger than 40% of the size of the pedestrian is visible. For this reason, the pedestrian detector  430  can recognize the pedestrian (a recognizable state). This process is executed for each depth distance Z. 
         [0076]    As the pedestrian, a pedestrian having a body shape (a standard size) with a height of 1.8 m is supposed. Then, the size of the pedestrian P on the image  1301  in appearance is calculated for each depth distance Z from 1 m to 5 m. Here, a maximal percentage of the pedestrian P hidden by the particulate deposit  1302  (a ratio in which a recognition object having a standard size is hidden by a particulate deposit region) is calculated by the comparison of the shape of the pedestrian P in each depth and a region part (a particulate deposit region) in which a background ot visible due to the particulate deposit  1302  such as mud. For example, a depth in which 30% or more of the pedestrian P is not visible to maximal and a viewing angle θ from the camera  101  are calculated. 
         [0077]      FIGS. 13-3 ( a ) and  13 - 3 ( b ) illustrated examples in which a sensing disabled range  1331  incapable of recongnizing (sensing) the pedestrian and a sensing enabled range  1332  capable of recognizing (sensing) the pedestrian are displayed on a display unit  1330  such as an in-vehicle monitor. The sensing range determination unit  300  determines the sensing enabled range capable of sensing the pedestrian and the sensing disabled range incapable of sensing the pedestrian by the lens state diagnosed by the lens state diagnosis unit  200  when the application is executed. 
         [0078]    In the example illustrated in  FIG. 13-3 ( a ), the sensing disabled range  1331  is set such that the pedestrian farther than a predetermined distance  705  is not visible in response to the shape or the size of the particulate deposit. The predetermined distance  705  is set such that a position moves close to the vehicle  1  as the size of the particulate deposit becomes large and a position moves away from the vehicle  1  as the size of the particulate deposit becomes small. An angle θ determining the horizontal width of the sensing disabled range  1331  is set in response to the size of the particulate deposit. Then, in the example of  FIG. 13-3 ( b ),particulate deposit adheres to the in-vehicle camera  101   a  attached to the front part of the vehicle  1 . Here, since there is a high possibility that a far position is not visible due to the influence of the particulate deposit, a position farther than the predetermined distance  705  of the image captured by the in-vehicle camera  101  installed at the front part of the vehicle cannot be used. 
         [0079]    &lt;Vehicle Detection&gt; 
         [0080]    A concept of a vehicle detection is similar to that of the pedestrian detection and a vehicle M corresponding to a recognition object has a width of 1.8 m and a depth of 4.7 m. Then, a difference from the pedestrian P is that a direction of the vehicle M corresponding to the detection object is the same as a direction in which a lane is recognized or an own vehicle travels. A calculation is made on the assumption that the vehicle is a preceding vehicle or a preceding vehicle traveling on an adjacent vehicle lane in the same direction. For example, as illustrated in  FIG. 14-1 ( a ), a case in which a preceding vehicle M traveling on a lane WL overlaps the left upper particulate deposit  1302  will be examined in each depth. Since the vehicle N is larger than the pedestrian P, it is possible to detect a position farther than the pedestrian P. Here, when 40% or more of the vehicle body is hidden, it is determined that the detection is not easily guaranteed. Since the vehicle M is a rigid body compared to the pedestrian P and an artificial object, it is possible to guarantee the detection even when the hidden percentage (ratio) increases compared to the pedestrian P. For example, as illustrated in  FIGS. 14-2 ( a ) and  14 - 2 ( b ), since the percentage in which the particulate deposit  1302  shields the vehicle M increases as the vehicle M is located at a far position, it is difficult to guarantee a detection at a far position and a detection in the front direction of the front fish-eye camera. In the example illustrated in  FIG. 14-2 ( a ), since the preceding vehicle is separated from the own vehicle by 7.0 m, a vehicle detector  420  cannot recognize the vehicle (an unrecognizable state). Further, in the example illustrated in  FIG. 14-2 ( b ), since the preceding vehicle is separated from the own vehicle by 3.0 m, the vehicle detector  420  can recognize the vehicle (a recognizable state). 
         [0081]    &lt;Lane Recognition&gt; 
         [0082]    A basic concept of a lane recognition is similar to that of the pedestrian detection or the vehicle detection. A difference is that a size of the recognition object is not set. However, it is important that, since the lane WL is recognized from a far position of 10 m to the vicinity of 50 cm, an invisible range from a certain meter position to a certain meter position is detected. Then, it is determined whether a stain region on a screen is hidden in a certain range on the road by the use of the geometry of the camera. 
         [0083]    In the case of a white line (the lane WL), the right recognition performance using parallelism is influenced when a far left side is not visible. For this reason, when it is determined that a left position farther than 5 m is not visible, it is determined that a far right side of the white line cannot be recognized due to the same performance. Even in an actual image process, an erroneous detection may be reduced by an image process excluding a position farther than 5 m. Alternatively, only the stain region maybe excluded from the sensing region. While a detection guarantee range is suggested, it is determined whether the detection guarantee range can be used for a control, can be used for a warning instead of a control, or cannot be used for any purpose in consideration of the accuracy of the horizontal position, the yaw angle, and the curvature of the lane recognition deteriorating as a detection guarantee region becomes narrow. 
         [0084]    &lt;Parking Frame Detection&gt; 
         [0085]    A parking frame exists on the road as in the white line, but an approximate size of an object can be regarded as a given size differently from the white line. Of course, there is a slight difference in the size of the parking frame depending on a place. However, for example, a parking frame having a width of 2.2 m and a depth of 5 m is defined and the possibility of the hidden percentage inside the frame of the region is calculated. In fact, since only a frame line is important, the parking frame can be detected even when only the inside of the frame becomes dirty due to mud. However, when the parking frame is not visible due to the movement of the vehicle, the performance of the application cannot be guaranteed. Thus, the possibility of the hidden percentage inside the frame due to mud is calculated. When the percentage exceeds 30%, an operation cannot be guaranteed. This calculation is also executed for each depth. Further, the application using the parking frame is used for a parking assist in many cases while the vehicle is turned. For this reason, even when 30% or more of mud adheres to a position farther than 7 m at the left side of the front camera in the depth direction, a range capable of guaranteeing the application is defined as the vicinity within 7 m in the front camera. 
         [0086]    &lt;Barrier Detection&gt; 
         [0087]    In a barrier detection, all three-dimensional objects existing around the vehicle are defined as detection objects and thus the size of the detection object cannot be defined. For this reason, in the barrier detection, a case in which a foot of a three-dimensional object existing on the road cannot be specified is defined as a case in which the barrier detection performance cannot be guaranteed. For this reason, a basic concept is supposed on the assumption that a road region having a certain size is reflected on a mud detection region. Then, an invisible distance due to a shielding ratio increasing at a certain range from the own vehicle is obtained by conversion and thus the barrier detection performance guarantee range is determined. For example, as illustrated in  FIG. 15( a ) , when the particulate deposit  1302  adheres to the lens so that a region in which an arrow in the up direction is not visible exist, this region can be determined as a region in which a background is not visible due to the particulate deposit, that is, a sensing disabled range  1303  can be determined as illustrated in  FIG. 15( b ) . 
         [0088]    In this way, in the vehicle detection or the pedestrian detection capable of assuming the approximate three-dimensional size of the detection object corresponding to the three-dimensional object, the three-dimensional object having a certain size and corresponding to the detection object is assumed and a percentage in which the three-dimensional object is shielded by a certain degree of a stain on the image is calculated when the three-dimensional position is changed in the depth direction on the road and the horizontal direction perpendicular thereto. Here, an unrecognizable three-dimensional position is determined when the percentage shielded by the particulate deposit exceeds a threshold value and a recognizable three-dimensional position is determined when the percentage does not exceed the threshold value. 
         [0089]    In this way, when the durable shielding ratio of the object in each application illustrated in  FIG. 16  is calculated in the particulate deposit state, a position where a detection object detection rate decreases is estimated as a three-dimensional region based on the own vehicle. Here, when the object size is not defined as in the barrier detection, a certain size at a foot position is assumed and the visible state of the region may be determined instead. 
         [0090]      FIG. 16  is a table showing a durable shielding ratio and a standard size of the recognition object of the application. Here, the durable shielding ratio indicates a state where the recognition object can be recognized when the size of the particulate deposit on the image is smaller than the size of the recognition object by a certain percentage. For example, when the particulate deposit is 50% or less of the size of the vehicle in the vehicle detection, the vehicle can be recognized. Further, when the particulate deposit is 40% or less of the size of the pedestrian in the pedestrian detection, the vehicle can be recognized. In this way, when the sensing enabled range of the camera is estimated in the three-dimensional region on the image, the sensing enabled range changing in response to the lens state of the camera can be easily notified to the user. 
         [0091]    &lt;Sharpness Distance Calculation Unit  320 &gt; 
         [0092]    In the sharpness distance calculation unit  320  illustrated in  FIG. 3 , a guaranteed detection distance is calculated on the basis of the average value Blave of the sharpness obtained by the sharpness detector  220 . First, standard sharpness α1 of the lens sharpness necessary for obtaining the edge strength used to recognize the recognition object to the maximal detection distance in each application is set.  FIG. 18( a )  is a diagram showing a relation between the maximal detection distance and the edge strength of each application. Then, when the sharpness is equal to or larger than the standard sharpness α1, each application can guarantee a sensing operation to the maximal detection distance. However, the guaranteed detection distance from the maximal detection distance becomes shorter as the sharpness becomes lower than the standard sharpness α1. The sharpness distance calculation unit  320  shortens the guaranteed detection distance as the sharpness decreases from the standard sharpness α1. 
         [0093]      FIG. 18( b )  is a graph showing a relation between a detection distance and sharpness. Here, when the sharpness Blave exists between the standard sharpness al and the minimal sharpness α2, the guaranteed detection distance of the application changes. 
         [0094]    Regarding the setting of each application, as illustrated in the table of  FIG. 18( a ) , when the maximal detection distance of each application exists and a range of the maximal detection distance is guaranteed, the standard sharpness α1 or more set for each application needs to be indicated by the average value Blave of the sharpness. As the average value Blave of the sharpness decreases from the standard sharpness α1, the guaranteed detection distance decreases. When the sharpness reaches the minimal sharpness α2 of the target application, the detection is not available. 
         [0095]    For example, when the application is for the vehicle detection, the maximal detection distance becomes 10 m when the standard sharpness is 0.4 and the minimal detection distance becomes 0 m when the minimal sharpness is 0.15. Then, when the application is for the pedestrian detection, the maximal detection distance becomes 5 m when the standard sharpness is 0.5 and the minimal detection distance becomes 0 m when the minimal sharpness is 0.2. 
         [0096]      FIGS. 17( a ) and 17( b )  are diagrams showing a method of determining the sensing enabled range by the sensing range determination unit  300  in response to the sharpness.  FIG. 17( a )  shows an example in which the low sharpness state is displayed on the in-vehicle monitor and  FIG. 17( b )  shows an example in which the sensing disabled range  1331  incapable of recognizing (sensing) the pedestrian and the sensing enabled range  1332  capable of recognizing (sensing) the pedestrian are displayed on the display unit  1330  such as an in-vehicle monitor. 
         [0097]    For example, when the sharpness is low due to cloudness as illustrated in  FIG. 17( a ) , there is a high possibility that a far position is not visible. For this reason, as illustrated in  FIG. 17( b )  it is defined that a position farther than the predetermined distance  705  in the image captured by the in-vehicle camera  101  installed at the front part of the vehicle cannot used. The predetermined distance  705  is set such that a position moves close to the vehicle  1  as the sharpness becomes closer to the minimal sharpness and a position moves away from the vehicle  1  as the sharpness becomes closer to the standard sharpness. 
         [0098]    &lt;Water Droplet Distance Calculation Unit  330 &gt; 
         [0099]    In the water droplet distance calculation unit  330  illustrated in  FIG. 3 , the sensing enabled range for each application is calculated on the basis of the result of the water droplet detector  230 . A region within a process region of each application and having a score SB (x, y) exceeding the threshold value ThrB is calculated on the basis of the threshold value ThrB and the score SB (x, y) obtained as a result of the water droplet detection. Since this is obtained as a numerical value indicating the amount of the water droplet within the process region of each application, the water droplet occupying ratio is obtained for each application (each recognition application) in such a manner that an area of a water droplet adhering region (an area of a water droplet region corresponding to a region in which the water droplet adheres) within the process region of the application is divided by an area of the process region. 
         [0100]    By using the water droplet occupying ratio, the maximal detection distance is determined. As illustrated in  FIG. 19( a ) , there is a high possibility that the lens state promptly changes in the case of a water droplet  1902 . For example, the lens state may change due to the water droplet of falling rain or from the road or the water droplet amount may be reduced due to the opposite traveling wind or the heat generated during the activation of the camera. Likewise, there is a high possibility that the lens state may change at all times. For this reason, it is possible to prevent a determination that the position  1903  is in a region which is out of a viewing angle or cannot be detected due to the position of the current water droplet. In the lens state involving with the current water droplet adhering amount, a far position or a small object position cannot be correctly determined. Accordingly, an operation depending on the lens state is guaranteed in such a manner that the detection distance is set to be short. 
         [0101]    Since the process region is different for each application, the water droplet distance calculation unit  330  calculates the Guaranteed detection distance from the water droplet occupying ratio obtained in consideration of the process region. Further, the water droplet occupying ratio capable of guaranteeing the maximal detection distance of the application from the value of the water droplet occupying ratio is set as the durable water droplet occupying ratio illustrated in  FIG. 20( a ) . Further, the water droplet occupying ratio incapable of guaranteeing the detection and the operation of the application is set as the limited water droplet occupying ratio. The limited water droplet occupying ratio state indicates a state where the guaranteed detection distance is 0 m. Here, the guaranteed detection distance from the durable water droplet occupying ratio to the limited water droplet occupying ratio decreases linearly as illustrated in  FIG. 20( b ) . 
         [0102]    The image of the background is not easily visible when the water droplet adheres to the lens. Thus, the image may be erroneously detected or may not be detected for an image recognition logic as the water droplet adhering amount on the lens increases. For this reason, since the water droplet adhering amount within the range of the image process for recognizing the recognition object among the water droplets adhering to the lens is obtained, the water droplet adhering amount is used while being converted to a degree causing an erroneous detection or a non-detection in each application (water droplet durability). For example, when the water droplet occupying ratio within the process region of the lane recognition is high, a large water droplet amount exists in the region where a lane exists on the image. Accordingly, there is a possibility that the lane cannot be appropriately recognized. Here, as for the detection of a far position which can be easily influenced by the distortion of the water droplet, the guaranteed target is not ensured at a level in which the water droplet occupying ratio is slightly raised and the guaranteed target is not ensured even in a near distance in response to an increase in water droplet occupying ratio. 
         [0103]    For example, even when the application in execution is the vehicle detection, the maximal detection distance of 10 m can be guaranteed until the water droplet occupying ratio becomes 35% or less of the durable water droplet occupying ratio 35%. Here, when the water droplet occupying ratio becomes larger than 60% of the limited water droplet occupying ratio, the minimal detection distance becomes 0 m. Then, when the application is the pedestrian detection, the maximal detection distance of 5 m can be guaranteed when the water droplet occupying ratio becomes 30% of the durable water droplet occupying ratio. Then, the minimal detection distance becomes 0 m when the water droplet occupying ratio becomes larger than 50% of the limited water droplet occupying ratio. 
         [0104]      FIG. 4  is a block diagram showing an internal function of the application execution unit  400 . 
         [0105]    The application execution unit  400  includes, for example, a lane recognition unit  410 , a vehicle detector  420 , a pedestrian detector  430 , a parking frame detector  440 , and a barrier detector  450  to be executed on the basis of a predetermined condition. 
         [0106]    The application execution unit  400  executes various applications used for recognizing the image in order to improve the preventive safety or the convenience by using the image captured by the in-vehicle camera  101  as an input. 
         [0107]    The lane recognition unit  410  executes, for example, the lane recognition used to warn or prevent a vehicle lane departure, to conduct a vehicle lane keep assist, and to conduct a deceleration before a curve. In the lane recognition unit  410 , a feature amount of the white line WL is extracted from the image and the linear property or the curved property of the feature amount is evaluated in order to determine whether the own vehicle exists at a certain horizontal position within the vehicle lane or to estimate a yaw angle representing an inclination with respect to the vehicle lane and a curvature of a travel vehicle lane. Then, when there is a possibility that the own vehicle may depart from the vehicle lane in response to the vehicle horizontal position, the yaw angle, or the curvature, an alarm for warning the risk to the driver is generated. Alternatively, when there is a possibility that the vehicle lane departure may occur, a control of returning the own vehicle to the own vehicle lane in order to prevent the departure is executed. Here, when the vehicle is controlled, there is a need to stabilize the vehicle lane recognition performance and to highly accurately obtain the horizontal position and the yaw angle. Further, when the vehicle lane to a far position can be extracted with high accuracy, an assist may be executed which has high curvature estimation accuracy, can be used for a control on a curve, and can support a smooth curve travel operation. 
         [0108]    The vehicle detector  420  extracts a square shape on the image of the rear face of the preceding vehicle as a feature amount in order to extract a vehicle candidate. It is determined that the candidate is not a stationary object by checking whether the candidate moves on the screen at the own vehicle speed differently from the background. Further, the candidate may be narrowed by the pattern matching for a candidate region. In this way, when the vehicle candidate is narrowed to estimate the relative position with respect to the own vehicle, it is determined whether the own vehicle may contact or collide with the vehicle candidate. Accordingly, it is determined whether the vehicle candidate becomes a warning target or a control target. In the application used to follow the preceding vehicle, an automatic following operation with respect to the preceding vehicle is executed by the control of the own vehicle speed in response to the relative distance of the preceding vehicle. 
         [0109]    The pedestrian detector  430  narrows a pedestrian candidate by extracting a feature amount based on a head shape or a leg shape of a pedestrian. Further, a moving pedestrian is detected on the basis of a determination reference indicating a state whether the pedestrian candidate moves in a collision direction by the use of a comparison of a movement of a background of a stationary object moving along with the movement of the own vehicle. By the pattern matching, the stationary pedestrian may be also used as a target. In this way, when the pedestrian is detected, it is possible to execute a warning or control process depending on whether the pedestrian jumps into the own vehicle lane. Further, it is possible to obtain an application which is very helpful for a low-speed region such as a parking place or an intersection instead of a road travel state. 
         [0110]    The parking frame detector  440  extracts a white line feature amount similarly to the white line recognition when the vehicle travels at a low speed, for example, 20 km or less. Next, all lines having different inclination degrees and existing on the screen are extracted by Hough transformation. Further, a parking frame is checked to assist the driver&#39;s parking operation instead of searching for a simple white line. It is checked whether the horizontal width of the parking frame is a width in which the vehicle  1  needs to be stopped or the vehicle  1  can be parked in a parking region by detecting a bumper block or a white line at the front or rear side of the vehicle  1 . When the parking frame is visible to a far position in a wide Parking lot, the user can select a suitable parking frame from a plurality of parking frame candidates. However, only when a near parking frame is visible, the user needs to approach a near parking space in order to recognize the parking frame. Further, since the recognition is basically used for the parking control of the vehicle  1 , the user is informed of the non-control state when the recognition is not stable. 
         [0111]    The barrier detector  450  extracts a feature point on an image. The feature point having an original feature on an image including an angle for an object may be considered as a feature point having the same feature when a change on the image is small even at the next frame. By the use of the feature points between two frames or multiple frames, a three-dimensional restoration is executed. At this time, a barrier which may collide with the own vehicle is detected. 
         [0112]      FIG. 5  is a block diagram showing an internal function of the notification control unit  500 . 
         [0113]    The notification control unit  500  includes, for example, a warning unit  510 , a control unit  520 , a display unit  530 , a stain removing unit  540 , an LED display unit  550 , and the like. 
         [0114]    The notification control unit  500  is an interface unit that receives the determination result of the sensing range determination unit  300  and transmits the information to the user. For example, in a normal state where a sensing disabled range does not exist in a sensing range necessary for the application and the entire sensing range becomes a sensing enabled range, a green LED is turned on. Then, a green LED is turned on and off in a suppression mode. Then, in a system give-up state having a temporary possibility of an early return due to a rain or the like, an orange LED is turned on. Meanwhile, in a system give-up state having a low possibility of a return unless the lens is wiped by the user due to a durable stain such as mud or cloudness on the lens, a red LED is turned on. In this way, a system configuration is obtained in order to warn a current preventive safety application operation state and an abnormal state caused by the stain of the lens of a current system to the user. In addition, the system give-up state indicates a state where an application for recognizing a recognition object is stopped for the preventive safety when it is determined that an image suitable for an image recognition cannot be captured due to the particulate deposit on the lens surface. Further, the system give-up state indicates a state where a CAN output is stopped even when the recognition is not stopped or a warning corresponding to a final output or a recognition object recognition result is not transmitted to the user during a vehicle control or a display on a screen or even when a CAN output is generated. In the system give-up state, the give-up state of the recognition system may be notified to the user through a display or a voice while the recognition object recognition result is not notified to the user. 
         [0115]    In addition, when the preventive safety application is temporarily changed to the system give-up state, this transition state may be notified to the user through a display for warning the stop of the preventive safety application or a voice for warning the stop of the preventive safety application while not disturbing the driving operation of the driver. In this way, a function of notifying the transition of the application of the lane recognition or the vehicle detection to the stop state to the user may be provided. Further, a return state may be notified to the user through a display or a voice. Further, in a situation in which visibility is not improved by a road structure tracking unit although the lens state is improved when it is determined that a durable stain adheres to the lens, the lens may be improved after an orange display is selected as a failure display in a durable give-up state. In fact, there is also a possibility influenced by the light source or the background. Further, when it is determined that a durable stain other than a water droplet adheres to the lens so that a particularly red LED is turned on, an instruction may be Given to the user so that the lens is wiped by the user when the vehicle is stopped or before the vehicle starts to travel. 
         [0116]    Since the user is informed of the application operation state based on the lens state diagnosed by the lens state diagnosis unit  200  and the sensing enabled range determined by the sensing range determination unit  300 , it is possible to prevent a problem in which a preventive safety function is stopped without a notice to the user. 
         [0117]    When the user is informed of the current system state so that the user does not doubt the failure of the system and the vehicle lane departure is warned to the user during the operation of the vehicle lane recognition, an improvement treatment method using a lens wiping and clearing hardware is notified to the user. 
         [0118]    When a situation is not easily improved unless the user removes the stain of the lens surface in the system give-up state caused by the stain of the lens, this state is notified to the user. Accordingly, a further improvement request is notified to the user and a non-operation state of a current application is notified to the user. 
         [0119]      FIG. 21  is a diagram comparing the sensing enabled range in response to the recognition object. 
         [0120]    When the particulate deposit adhering to the front in-vehicle camera  101  has the same size and position and the recognition object of the application corresponds to three kinds of recognition objects, that is, a vehicle, a pedestrian, and a barrier, the size of the recognition object is different in each application and thus the sensing range is also different. For example, when the recognition object is the vehicle, a forward vehicle length La 2  of a minimum sensing range  2101  and a forward vehicle length La 1  of a maximum sensing range  2102  are longer than a forward vehicle length Lp 2  of a minimum sensing range  2111  and a forward vehicle length Lp 1  of a maximum sensing range  2112  of the pedestrian and a forward vehicle length Lm 2  of a minimum sensing range  2121  and a forward vehicle length Lm 1  of a maximum sensing range  2122  of the barrier are smaller than the forward vehicle length Lp 2  of the minimum sensing range  2111  and the forward vehicle length Lp 1  of the maximum sensing range  2112  of the pedestrian. Meanwhile, an angle θ in which a background is hidden by the particulate deposit is substantially the same among the applications, but is corrected in response to the size of the recognition object. 
         [0121]    According to the surrounding environment recognition device  10  of the invention, it is possible to notify the sensing enabled range set in response to the stain of the lens of the in-vehicle camera  101  to the user and to allow the user to check a range capable of recognizing the recognition object of the application. Thus, it is possible to allow the user to drive the vehicle while further keeping an eye on the surrounding environment by preventing a careless attention on the surrounding environment due to the overestimation of the application. 
         [0122]    While the embodiment of the invention has been described, the invention is not limited to the above-described embodiment and various modifications in design can be made without departing from the spirit of the invention of claims. For example, the above-described embodiment has been carefully explained for the easy comprehension of the invention and all configurations may not be essentially provided. Further, a part of a configuration of a certain embodiment may be replaced as a configuration of the other embodiment and a configuration of the other embodiment may be added to a configuration of a certain embodiment. Furthermore, the other configurations may be added to, deleted from, or replaced by a part of a configuration of each embodiment. 
       REFERENCE SIGNS LIST 
       [0000]    
       
           10  surrounding environment recognition device 
           100  image capturing unit 
           200  lens state diagnosis unit 
           210  particulate deposit detector 
           220  sharpness detector 
           230  water droplet detector 
           300  sensing range determination unit 
           310  particulate deposit distance calculation unit 
           320  sharpness distance calculation unit 
           330  water droplet adhering distance calculation unit 
           400  application execution unit 
           410  lane recognition unit 
           420  vehicle detector 
           430  pedestrian detector 
           440  parking frame detector 
           450  barrier detector 
           500  notification control unit 
           510  warning unit 
           520  control unit 
           530  display unit 
           540  stain removing unit 
           550  LED display unit

Technology Category: g