JPH09113221A - On-vehicle device and method for processing picture - Google Patents

Abstract

PROBLEM TO BE SOLVED: To prevent the erroneous recognition of the traveling environment of a vehicle and, at the same time, to quickly detect the abnormality of an image and discriminate the kinds of the abnormality by computing the traveling environment by detecting the edges of a picked-up image and, when the abnormality of the image is detected by comparing the amount of the edges with a reference value, inhibiting the computation of the traveling environment. SOLUTION: Image information picked up with a CCD camera 1 is stored in an image memory 3 after A/D conversion 2 and the stored information is processed by means of a CPU 4. The CPU 4 is incorporated with a means 7 for detecting abnormality of image pickup circuit, a means 8 for inhibiting computation of traveling environment, and a means 9 for inhibiting detection of abnormality in addition to a means 5 for detecting position of preceding vehicle and a mean 6 for detecting position of white line which work as traveling environment computing means. The means 5 and 6 compute the traveling environment of their own vehicle by detecting the edges of the image picked up with the camera 1. The means 7 detects the abnormality of an image by comparing the amount of the detected edges with a reference value and, when the means 7 detects the abnormality of the image, the inhibiting means 8 inhibits the computing processes of the means 5 and 6.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a vehicle-mounted image processing apparatus for calculating a traveling environment based on an image obtained by picking up an image around a vehicle, and an image processing method thereof.

[0002]

2. Description of the Related Art Conventionally, there is a method in which an image of a vehicle surroundings obtained by a CCD camera or the like is subjected to image processing to calculate a traveling environment. The traveling environment is the position of the white line on the road, the presence or absence of a vehicle traveling in front of the host vehicle, or the position thereof. An example of such an apparatus is disclosed in Japanese Patent Laid-Open No. 3-273498. The device shown in the publication is
The white line position is recognized based on the vehicle peripheral image input from the image input unit. The white line position is recognized by detecting an edge image from the vehicle peripheral image and binarizing it.

[0003]

Since the conventional apparatus only processes the image obtained by the CCD camera to calculate the traveling environment, it is possible to detect even if there is an abnormality in the image pickup unit such as the CCD camera. could not. Therefore, image processing may be performed using an inappropriate image, and the running environment may be erroneously recognized.

The present invention has been made in order to solve the above-mentioned problems, and detects an abnormality in an image, and when the image is abnormal, prohibits the calculation of the traveling environment to prevent erroneous recognition of the traveling environment. It is an object of the present invention to provide an in-vehicle image processing device which has been impossible in the past.

Another object of the present invention is to provide a vehicle-mounted image processing apparatus which detects an image abnormality by increasing the frequency with which an image abnormality detection process is performed.

Another object of the present invention is to provide an in-vehicle image processing apparatus capable of discriminating the type of image abnormality.

Another object of the present invention is to speed up the processing in a vehicle-mounted image processing apparatus capable of detecting an abnormal image.

Another object of the present invention is to provide a highly reliable on-vehicle image processing apparatus which prohibits the processing for detecting an abnormality when there is a risk of erroneously detecting an abnormality in an image.

Further, the present invention detects an abnormality in an image,
It is an object of the present invention to provide an image processing method that has never existed in the past, in which calculation of a traveling environment is prohibited when an image is abnormal and erroneous recognition of the traveling environment can be prevented.

[0010]

An on-vehicle image processing apparatus according to the present invention includes an image pickup means for picking up an image of the periphery of a vehicle, an edge detecting means for detecting an edge of an image picked up by the image pickup means, and an edge of the image. An image abnormality is detected by comparing the amount of edges and a reference value with the amount of edges detected by the edge detection means, and the amount of edges detected by the edge detection means. The image abnormality detecting means and the prohibiting means for prohibiting the calculation of the traveling environment calculating means when the image abnormality detecting means detects the abnormality of the image are provided.

In addition, the vehicle-mounted image processing apparatus according to the present invention alternately performs the driving environment calculation and the image abnormality detection calculation every predetermined period.

Further, in the vehicle-mounted image processing apparatus according to the present invention, the reference value is the first threshold value for determining whether the contrast of the image is less than or equal to a predetermined value, and an edge of a predetermined value or more is generated in the image. The image abnormality detecting unit has a second threshold value for determining whether or not the image has been calculated, and an edge amount of the image calculated last time, and the image abnormality detecting unit uses the edge amount of the image calculated this time and three types of reference values. Are detected to detect an abnormality in the image, and three different abnormality detection signals are output according to the type of the abnormality in the image.

In the vehicle-mounted image processing apparatus according to the present invention, the edge amount calculation means sets a window in the image and calculates the amount of edges in the window.

Further, the vehicle-mounted image processing apparatus according to the present invention comprises an estimating means for estimating that the image is in an unstable state,
An abnormality detection prohibition unit that prohibits the abnormality detection of the image when the estimation unit estimates that the image is in an unstable state is provided.

Further, the image processing method according to the present invention comprises the steps of capturing an image of the periphery of the vehicle, detecting the edges of the image captured in this step, and calculating the traveling environment based on the edges of the image. A step of calculating the amount of edges of the image, a step of detecting an abnormality of the image by comparing the amount of edges with a reference value, and a step of calculating the traveling environment when the abnormality of the image is detected in this step. It consists of prohibiting steps.

[0016]

BEST MODE FOR CARRYING OUT THE INVENTION

Embodiment 1 FIG. 1 is a block diagram showing a simplified configuration of an in-vehicle image processing device according to a first embodiment of the invention. In the figure, reference numeral 1 denotes a CCD camera as an image pickup means for picking up an image of the surroundings of a vehicle, which picks up an image of the front of the vehicle. Reference numeral 2 is an A / D (analog / digital) converter for converting an image signal, which is image information of an image captured by the CCD camera 1, into a digital signal, and 3 is an image memory for storing the digital signal from the A / D converter 2. Reference numeral 4 denotes C which performs various processing using the image information stored in the image memory 3.
PU. Reference numeral 5 is a preceding vehicle position detecting means for detecting the position of a preceding vehicle traveling in front of the own vehicle, and 6 is white line position detecting means for detecting the position of a white line drawn on the road. The preceding vehicle position detecting means 5 and the white line are shown. The position detecting means 6 is an example of a traveling environment calculating means for calculating a traveling environment. 7 is a CCD camera 1
The CCD camera 1 and the A / D converter 2 are detected by detecting that the image is abnormal by the imaging circuit abnormality detecting means as the image abnormality detecting means for detecting the abnormality of the image captured in
Alternatively, the abnormality of the image memory 3 or the like is detected. Reference numeral 8 denotes a traveling environment calculation prohibiting means as a prohibiting means for prohibiting the arithmetic processing of the traveling environment such as the preceding vehicle position detecting means 5 and the white line position detecting means 6 when the imaging circuit abnormality detecting means 7 detects an abnormality in the image. Is an abnormality detection prohibition means for prohibiting the abnormality detection processing of the imaging circuit abnormality detection means 7 when the image is estimated to be in an unstable state. The preceding vehicle position detecting means 5
The abnormality detection prohibiting means 9 is included in the CPU 4.

FIG. 2 is a flowchart showing a processing procedure according to the first embodiment of the invention, and this calculation is performed by the CPU 4. The flowchart of FIG. 2 is the main routine. The calculation performed by the CPU 4 is roughly divided into two modes. One mode is a normal processing mode in which the running environment such as white line position detection processing and preceding vehicle detection processing is calculated based on the information of the image captured by the CCD camera 1.
The other mode is a failure diagnosis mode as image abnormality detection processing for detecting whether or not there is abnormality in the image captured by the CCD camera 1. The normal processing mode is composed of steps 102 to 104 described later, and the failure diagnosis mode is composed of steps 105 to 108 described later.

The processing procedure of FIG. 2 will be described. Step 101 determines which of the normal processing mode and the failure diagnosis mode is selected. The two modes are alternately selected every predetermined period. Set the predetermined period by CP
A counter in U4 may be used for every predetermined time,
The odometer may be used for each predetermined distance, or the number of ignitions of the internal combustion engine may be counted and set for each predetermined number of ignitions. When the normal processing mode is selected, the process proceeds to step 102. In step 102, a white line position detection process described below is performed to detect the position of the white line drawn on the road. In step 103, a preceding vehicle detection process, which will be described later, is performed to detect the presence / absence of a preceding vehicle traveling ahead of the host vehicle and its position. Step 104 is an error determination process. In this step, if neither the white line position nor the preceding vehicle is detected for a predetermined time or longer in the white line position detection processing and the preceding vehicle detection processing, it is considered that the image is abnormal. In that case, the process proceeds to step 105 and the failure diagnosis mode is executed. . If the white line position or the preceding vehicle is detected, it is determined that there is no problem and the process returns to step 101.

The processing from step 105 onward is the processing in the failure diagnosis mode. This failure diagnosis mode processing is performed when the failure diagnosis mode is selected in step 101 or when it is determined in step 104 that there is a possibility of image abnormality. In step 105, C
It is determined whether or not the environment is one in which the CD camera 1 can operate normally. First, it is conceivable that the CCD camera 1 may not operate normally immediately after the device is started because the power supply system is unstable. Therefore, it is confirmed that a predetermined time has passed since the device was started. Further, it is generally considered that the window is fogged or frosted when the defogger switch is ON. In such a state, the CCD camera 1 cannot accurately capture the situation in front of the vehicle. Therefore, make sure that the defogger switch is off. In addition, this device is premised to operate when traveling on a road. It does not operate normally in situations other than driving on the road, such as when entering or leaving a garage. Therefore, it is confirmed by a vehicle speed sensor that the vehicle is traveling at a vehicle speed of 40 km / h or more. Generally, if the vehicle is traveling at a vehicle speed of 40 km / h or more, it is presumed that the vehicle is traveling on the road. If all of the above conditions are satisfied, it is estimated that the CCD camera 1 is in a state in which it is possible to accurately capture the situation in front of the vehicle, and the process proceeds to step 106 in the subsequent stage. If any of the above conditions is not satisfied, it is estimated that the CCD camera 1 cannot accurately capture the situation in front of the vehicle, that is, the image is in an unstable state, and the failure diagnosis is not performed in step 101. Return to. Here, step 105 comprises estimating means for estimating that the image is in an unstable state and abnormality detection inhibiting means for inhibiting the abnormality detection of the image when the image is estimated to be in the unstable state. ing.

In step 106, the image abnormality detecting means carries out an image pickup circuit abnormality detecting process for diagnosing whether or not there is an abnormality in the image picked up by the CCD camera 1. Details of this processing will be described later. When the image pickup circuit abnormality detection processing in step 106 is completed, the process proceeds to step 107. When no image abnormality is detected in the image pickup circuit abnormality detection processing in step 107, the processing is returned to step 101. If an image abnormality is detected in the imaging circuit abnormality detection process, the process proceeds to the fail-safe process of step 108. Step 107 constitutes a prohibition means for prohibiting the calculation of the traveling environment when the abnormality of the image is detected. Step 108 is a step for performing fail-safe processing, and notifies the driver that an abnormality has occurred in the image,
In order to promptly perform the subsequent repair, processing such as storing what kind of abnormality has occurred in the image is performed. It should be noted that the driver is notified by visual means such as turning on a lamp on the instrument panel, or audibly indicating by voice.

Next, each subroutine will be described. First, the white line position detection processing in step 102 will be described. FIG. 3 is a flowchart showing the procedure of white line position detection processing. 4A and 4B are explanatory diagrams for explaining the state of the white line position detection processing. FIG. 4A shows an example of the image 50 captured by the CCD camera 1, and FIGS. 4B to 4E show the horizontal position of the image 50 on the horizontal axis. FIG. 4 is a graph showing the brightness value on the vertical axis, and is an explanatory diagram for explaining the process of FIG. 3. In the figure, 52 is a character drawn on the road, 53 is a white line, 54 and 55 are thresholds, 56
Is a signal indicating a white line. Scan line 5 in step 111
The edge detection process for detecting the edges existing on the upper part 1 is performed. The edge is detected by the absolute value of the difference between the brightness values of the adjacent pixels on the scanning line 51. Here, FIG. 4 (b)
Shows the luminance value of the pixel on the illustrated scanning line 51. When the above-mentioned edge detection processing is applied to this FIG. 4B, FIG. 4C is obtained. That is, the edge is shown in FIG.
As shown in (c), it is detected on the scanning line 51 at a position where the luminance value greatly changes. Step 111 constitutes edge detection means. Step 112 is a binarization process for detecting only edges of a certain level or higher. Specifically, as shown in FIG. 4C, the signal subjected to the edge detection processing is compared with a predetermined threshold value 54, and the threshold value 5 is set.
Only edges of level 4 and above are extracted. This allows
The binarized information shown in FIG. 4D is obtained.

At step 113, time integration processing is performed. This time integration process is performed to remove the characters 52 and the like drawn on the road. When the binarized information of FIG. 4D is time-integrated, a large value can be obtained with a white line that appears at the same position as a white line 53 that is stable in time, but is unstable in time like a character 52. What appears,
That is, what appears occasionally is smoothed to a small value.
In the time integration process, the brightness value of an arbitrary pixel at a certain time point is P (n), and the time integration value of the same pixel at the same time point is I (n).
(N), I is the time integration value of the same pixel in the previous frame
If (n-1) and the time constant are [tau], it is calculated by the following equation.

[0023]

(Equation 1)

Here, if the time constant τ is large, the integration is long, and if it is small, the integration is short. FIG. 4 (e) is the result of performing the time integration process on FIG. 4 (d). Here, the values other than the portion corresponding to the white line 53 are small. In step 114, white line position detection processing is performed. In FIG. 4 (e), 55 is a threshold value provided to determine whether or not the line is a white line, and 56 exceeding the threshold value 55 is detected as a white line. Step 115 is a scan position update process, which updates the scan position of the scan line 51 from the current position to above the image. Here, the scanning of the image is
This is sequentially performed from the bottom of the image to the top.
Step 116 is for determining whether or not the entire surface of the image has been scanned. In this step, it is determined whether or not the processing has been completed up to the uppermost scanning line of the image 50, and if not completed, the process returns to step 111 to repeat the above-described processing, and if already completed, the processing is terminated, The process proceeds to step 103, which is the preceding vehicle detection process of the next stage, ends the white line position detection process, and returns to the main routine of FIG.

Next, the preceding vehicle detection processing will be described.
FIG. 5 is a flowchart showing the procedure of the preceding vehicle detection process. FIG. 6 is an explanatory diagram showing the state of the preceding vehicle detection process,
(A) is an example of an image 50 taken by the CCD camera 1,
(B) to (e) are graphs in which the horizontal axis represents the horizontal position of the image 50 and the vertical axis represents the brightness value, and are explanatory diagrams for explaining the processing of FIG. 5. In the figure, 57 is a preceding vehicle traveling in front of the host vehicle, 58 and 59 are threshold values, 60 is a signal indicating a white line,
Reference numeral 61 is a signal indicating the preceding vehicle. The edge detection process of step 121, the binarization process of step 122, and the time integration process of step 123 are the same as those described in the white line position detection process.

Now, the scanning line 51 is shown in FIG.
It is on 7. FIG. 4B shows the luminance value of the pixel on the scanning line 51. In step 121, edge detection processing is performed based on the absolute value of the difference between the brightness values of adjacent pixels on the scanning line 51, and the signal shown in FIG. 4C is obtained. In step 122, the signal subjected to the edge detection processing is compared with the threshold value 58 to be binarized as shown in FIG. The binarized signal is subjected to time integration processing in step 123 and becomes the signal of FIG. 5 (e). Step 124 is a preceding vehicle candidate search process, in which the signal subjected to the time integration process is compared with the threshold value 59, and a portion equal to or greater than the threshold value is set as a candidate position of the preceding vehicle. The candidates in this case are 60 and 61.
It is. Step 125 is the preceding vehicle position detection processing, which detects the position of the preceding vehicle from the above candidates. This is done by paying attention to the fact that an automobile has a predetermined width or more. A predetermined width is set in advance, and a candidate vehicle having a width equal to or larger than the predetermined width is a preceding vehicle. To detect as. Therefore, 61 is detected as the position of the preceding vehicle.

Step 126 is a scanning position updating process. If the preceding vehicle 57 can be detected in step 125, nothing is done in step 126 and the process proceeds to step 127.
The detected position information of the preceding vehicle 57 is output, the processing is terminated, and the process returns to the main routine of FIG. Step 12
If the preceding vehicle 57 is not found in step 5, the scanning position of the scanning line 51 is changed to a position higher than the present position in step 126, and the processes of steps 121 to 125 are repeated. Then, when the preceding vehicle 57 cannot be detected even when the uppermost scanning line of the image 50 is scanned, the preceding vehicle 5 is detected.
It is determined that the vehicle 7 is not present, the information that there is no preceding vehicle is output in step 127, the processing is terminated, and the process returns to the main routine of FIG.

Finally, the image pickup circuit abnormality detection processing will be described. FIG. 7 is a flowchart showing the processing procedure of the imaging circuit abnormality detection processing, and FIG. 8 is an image 5 taken by the CCD camera 1.
This is an example of 0. In step 131, the detection window 62 as a window is set in the image 50. The detection window 62 is set in an area where an edge is considered to exist when the image obtained by the CCD camera 1 is normal, and is set in a horizontally long shape near the center of the image as shown in FIG. Step 132 is an edge detecting process as an edge detecting means for detecting an edge in the detection window 62. Similar to the edge detection process described above, the edge is detected based on the absolute value of the difference between the brightness values of the adjacent pixels on the scanning line. Step 133 is the same as the above-described binarization process, and the binarization process is performed by comparing the signal subjected to the edge detection process with a predetermined threshold value.

Step 134 is an edge amount calculating means for calculating the amount of edges in the detection window 62, and here, horizontal histogram detection processing is performed. The horizontal histogram is obtained by totalizing pixel values in the horizontal direction of the obtained binarized information by scanning the entire detection window 62 and performing binarization processing. 9A and 9B are explanatory views for explaining the calculation of the horizontal direction histogram. FIG. 9A shows an example of an edge image obtained by performing binarization processing, and FIG. 9B shows a calculation of the horizontal direction histogram of this edge image. It was done.
In this example, the detection window 62 is set to have 28 pixels vertically and 38 pixels horizontally. The value of each pixel shown in FIG.
If ij, the horizontal histogram value Hsum is obtained by the following equation (2), and the histogram shown in FIG. 9B is obtained.

[0030]

(Equation 2)

The horizontal histogram thus obtained is compared with a predetermined threshold value in step 135. FIG. 10 shows an example in which the image obtained by the CCD camera 1 is abnormal. In FIG. 10A, the image is substantially white or almost black and there is not much contrast, that is, there are few edges, and FIG. 10B shows This shows a state in which noise is generated in the image and there are extremely many edges.
0 (a) and 0 (b) show horizontal histograms as in FIG. 9 (b). In step 135, the threshold 63
And 64. These thresholds constitute a reference value with which the amount of detected edges is compared, and the threshold 63
Is a first threshold value, and the threshold value 64 is a second threshold value.

The threshold value 63 is a reference for determining whether or not the image contrast is lower than a predetermined value. That is, FIG.
In (a), the region where the histogram value does not reach the threshold value 63 is in a low contrast state where the contrast is equal to or lower than a predetermined value, that is, there is not much contrast. C
Even if the image obtained by the CD camera 1 is in a normal state,
There are areas of low contrast. If this low contrast region is usually considered to be 10% or less of the entire area, the allowable range is set to 10%. For example, if the detection window 62 is 100 vertical by 150 horizontal pixels, 100
The allowable range is 10 lines, which is 10% of the lines. Therefore, in step 135, if there are 10 or more lines that do not reach the threshold value 63, it is determined that the image is in the low contrast state, and the error flag 1 is set.

Next, the threshold value 64 is a reference for determining whether or not the edges in the image are in a dense state above a predetermined level. That is, in FIG. 10B, the region where the histogram value exceeds the threshold value 64 is a region where the detected edges are extremely dense, that is, a region where extremely terrible noise is considered to have occurred in the image. Even if the image obtained by the CCD camera 1 is normal, there are some areas with dense edges. Therefore, as in the case of the low contrast described above, the allowable range of the area where noise appears is set. Then, if the line exceeding the threshold value 64 is equal to or larger than the set allowable range, it is determined that the image is in a state where severe noise is generated, and the error flag 2 is set.

When the threshold value comparison processing in step 135 is completed, the routine proceeds to step 136, where the histogram value calculated this time and the histogram value calculated last time are compared. Since the CCD camera 1 captures an image of the front of the running vehicle, the scenery changes as the vehicle runs. Therefore, the detected histogram should change from moment to moment. Therefore, in step 136, if the histogram value calculated this time and the histogram value calculated last time are substantially the same, it is determined that the image has not been updated, and the error flag 3 is set.

In step 137, error flag 1,
Check conditions 2 and 3. The states of the error flags 1, 2 and 3 are confirmed, and if none of the error flags is set, it is determined that there is no abnormality, and the processing ends. if,
If any of the error flags are set,
Returning to step 131, the above-mentioned processing is repeated. And
If the error flag in the set state continues to be set for a predetermined time or longer, it is determined that an abnormality has occurred.
For example, if the error flag 1 continues to be set, it is determined to be a contrast abnormality error, and if the error flag 2 continues to be set, it is determined to be a noise generation error, and the error flag 3 continues to be set. If the error occurs, it is determined that the error cannot be updated, and the abnormality detection signal is output. This abnormality detection signal is, for example, the above-mentioned error flags 1 to 3. When the process of step 137 ends, the abnormality detection process ends, and the process returns to the main routine of FIG. These error flags are stored as described in the failsafe processing of step 108 above.

In the above embodiment, the detection window 62 to be set has a horizontally long shape. This is because the horizontal edges of the image exist more steadily than the vertical edges, so that the detection window 62 having a horizontally long shape is considered to be effective. Therefore, as the shape of the detection window 62, a shape considered to be most effective may be adopted in accordance with the specifications of the image processing apparatus to which it is applied. The detection window is not always necessary. Instead of performing the arithmetic processing on the detection window, the arithmetic processing may be performed on the entire surface of the image.

Further, a horizontal histogram is presented as the calculation of the amount of edges. For the same reason, the horizontal edges of the image exist more steadily than the vertical edges, so it is more effective to use the horizontal histogram than the vertical histogram for the histogram. This is because

Although the histogram is shown as a method for detecting the amount of edges, the present invention is not limited to this, and a generally known amount measuring method may be used. The amount of edges referred to in the present invention includes the number of edges. That is, the number of edges in the horizontal direction is also taken into consideration as the amount of edges.

Furthermore, the above-mentioned various flow charts not only show the structure of the vehicle-mounted image processing apparatus, but also show the image processing method thereof.

As described above, according to the first embodiment, whether or not the image is normal is detected by calculating the edge amount and comparing it with the reference value. It is possible to obtain a vehicle-mounted image processing apparatus that has not been available in the related art and that can prevent calculation and prevent erroneous recognition of the traveling environment.

Further, since the normal processing mode and the failure diagnosis mode are alternately performed every predetermined period, each processing does not take a long time. Further, since the failure diagnosis mode is performed every predetermined period, the frequency of failure diagnosis increases and it becomes easy to detect an abnormality in an image.

Further, it is possible to discriminate what kind of abnormality the image has. Further, by storing the type of the abnormality, the repair can be promptly performed.

Since the detection window is set, the processing speed can be increased as compared with the case where arithmetic processing is performed on the entire surface of the image.

Further, since the abnormality detection of the image is prohibited in the state where the image may be unstable, the abnormality is not erroneously detected.

Further, according to the first embodiment, whether or not the image is normal is detected by calculating the edge amount and comparing it with the reference value, and if the image is abnormal, the running environment is calculated. It is possible to obtain an image processing method that has not been available in the past and that can be prohibited to prevent erroneous recognition of the traveling environment.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of a vehicle-mounted image processing apparatus according to a first embodiment.

FIG. 2 is a flowchart showing a processing procedure according to the first embodiment.

FIG. 3 is a flowchart showing a procedure of white line position detection processing.

FIG. 4 is an explanatory diagram illustrating a state of white line position detection processing.

FIG. 5 is a flowchart showing a procedure of a preceding vehicle detection process.

FIG. 6 is an explanatory diagram illustrating a state of a preceding vehicle detection process.

FIG. 7 is a flowchart illustrating a procedure of image pickup circuit abnormality detection processing.

FIG. 8 is an example of an image captured by a CCD camera.

FIG. 9 is an explanatory diagram illustrating a calculation of a horizontal direction histogram.

FIG. 10 is an example of a horizontal histogram when an image is abnormal.

[Explanation of symbols]

1: CCD camera, 2: A / D converter, 3: image memory, 4: CPU, 5: preceding vehicle position detecting means, 6: white line position detecting means, 7: imaging circuit abnormality detecting means, 8: running environment calculation Prohibition means, 9: abnormality detection prohibition means, 50: image, 5
1: scanning line, 52: character, 53: white line, 54, 55: threshold value, 56: signal indicating white line, 57: preceding vehicle, 58, 5
9: threshold value, 60: signal indicating white line, 61: signal indicating preceding vehicle, 62: detection window, 63, 64: threshold value

Claims (6)

[Claims] 1. An image pickup means for picking up an image around a vehicle, an edge detecting means for detecting an edge of an image picked up by the image pickup means, a running environment calculating means for calculating a running environment based on the edge of the image, Edge amount calculation means for calculating the amount of edges detected by the edge detection means, image abnormality detection means for detecting abnormality of the image by comparing the amount of edges with a reference value, and this image abnormality detection means And a prohibition unit that prohibits the calculation of the traveling environment calculation unit when the abnormality of the image is detected. 2. The vehicle-mounted image processing apparatus according to claim 1, wherein the calculation of the traveling environment and the abnormality detection calculation of the image are alternately performed every predetermined period. 3. The reference value is a first threshold value for determining whether or not the contrast of the image is less than or equal to a predetermined value, and for determining whether or not an edge of a predetermined value or more has occurred in the image. Of the edge of the image calculated previously, and the image abnormality detecting means compares the edge amount of the image calculated this time with the three types of reference values. The vehicle-mounted image processing apparatus according to claim 2, wherein the abnormality of the image is detected, and three different abnormality detection signals are output according to the type of abnormality of the image. 4. The vehicle-mounted image processing apparatus according to claim 1, wherein the edge amount calculation means sets a window on the image and calculates the amount of edges in the window. 5. An estimating means for estimating that the image is in an unstable state, and an abnormality detection inhibiting means for inhibiting abnormality detection of the image when the estimating means estimates that the image is in an unstable state. The vehicle-mounted image processing apparatus according to claim 1, further comprising: 6. A step of capturing an image of the periphery of a vehicle, a step of detecting an edge of an image captured in this step, a step of calculating a traveling environment based on the edge of the image, and an step of calculating an edge amount of the image. Steps to
The step of detecting an abnormality in the image by comparing the amount of the edge with a reference value; and the step of inhibiting the step of calculating the running environment when the abnormality in the image is detected in this step. An image processing method characterized by: