CN114677574A - Method and system for diagnosing image fault for automatic driving - Google Patents
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Abstract
The present disclosure relates to a method of diagnosing image failure for automatic driving, including: sliding the diagnosis window to scan the image to be diagnosed; and after the diagnosis window is slid every time, judging whether an image fault exists or not according to the number of the abnormal pixel points in the current diagnosis window and the average value and the variance of Euclidean distances among all the abnormal pixel points. The image fault diagnosis scheme is particularly suitable for deep learning of an automatic driving system, and innovatively provides evaluation criteria and a method for image faults of automatic driving so as to reduce calculation amount and improve diagnosis reliability, thereby improving automatic driving cruising performance of a vehicle.
Description
Technical Field
The present disclosure relates to the field of computer vision technology, and more particularly, to a method and system for image fault diagnosis, for example, for automated driving.
Background
Cameras are an important component of autonomous driving systems as a visual component of autonomous vehicles. The automatic driving system acquires various feature quantities from the camera image as a cruising basis for automatic driving. In the image processing process of the automatic driving system, whether the image has a fault or not needs to be diagnosed at first, and reliable basis and guarantee are provided for subsequent processing.
In the prior art, image fault diagnosis focuses more on detecting defective pixels, aiming at identifying defective pixels and the types of the defective pixels. However, the current defective pixel diagnosis method is not satisfactory in practical application, and there is a need and a space for further improvement because false alarm occurs often due to a large amount of calculation and a complex diagnosis system, which affects the reliability of diagnosis.
Disclosure of Invention
The present disclosure provides a method of diagnosing image failure for automatic driving, including:
sliding the diagnosis window to scan the image to be diagnosed;
and after the diagnosis window is slid every time, judging whether an image fault exists or not according to the number of the abnormal pixel points in the current diagnosis window and the average value and the variance of Euclidean distances among all the abnormal pixel points.
Advantageously, after each sliding of the diagnostic window, the following steps are performed:
counting the number of abnormal pixel points in the current diagnosis window;
if the number of the abnormal pixel points is less than or equal to the preset threshold of the number of the abnormal pixel points, continuously sliding the diagnosis window to scan the rest part of the image;
if the number of the abnormal pixel points is larger than a preset threshold of the number of the abnormal pixel points, calculating the average value and the variance of Euclidean distances among all the abnormal pixel points, generating image fault information when the average value is smaller than the preset threshold of the average value and the variance is smaller than a preset threshold of the variance, and continuously sliding the diagnosis window to scan the rest part of the image when the average value is larger than or equal to the preset threshold of the average value or the variance is larger than or equal to the preset threshold of the variance.
Advantageously, after each sliding of the diagnostic window, the following steps are performed:
counting the number of abnormal pixel points in the current diagnosis window, and calculating the average value and the variance of Euclidean distances among all the abnormal pixel points;
if the number of the abnormal pixel points is larger than a preset threshold of the number of the abnormal pixel points, the average value is smaller than a preset threshold of the average value, and the variance is smaller than a preset threshold of the variance, generating image fault information;
and if the number of the abnormal pixel points is less than or equal to a preset threshold of the number of the abnormal pixel points, or the average value is greater than or equal to a preset threshold of the average value, or the variance is greater than or equal to a preset threshold of the variance, continuing to slide the diagnosis window to scan the rest part of the image.
Advantageously, the length and width of the diagnostic window are set in accordance with a deep learning minimum identification region.
Beneficially, when the gray value of a pixel point in the image is not within the gray threshold range of the pixel point, the pixel point is determined to be an abnormal pixel point.
The present disclosure also provides a system for diagnosing image failure for automatic driving, including:
an image acquisition module arranged to receive images acquired in real time;
an image scanning module provided with a diagnosis window and scanning the image by sliding the diagnosis window;
and the fault diagnosis module judges whether an image fault exists according to the number of the abnormal pixel points in the current diagnosis window and the average value and the variance of Euclidean distances among all the abnormal pixel points after sliding the diagnosis window every time.
Advantageously, the fault diagnosis module is configured to perform the following diagnostic steps after each sliding of the diagnostic window:
counting the number of abnormal pixel points in the current diagnosis window;
if the number of the abnormal pixel points is less than or equal to the preset threshold of the number of the abnormal pixel points, continuously sliding the diagnosis window to scan the rest part of the image;
if the number of the abnormal pixel points is larger than a preset threshold of the number of the abnormal pixel points, calculating the average value and the variance of Euclidean distances among all the abnormal pixel points, generating image fault information when the average value is smaller than the preset threshold of the average value and the variance is smaller than a preset threshold of the variance, and continuously sliding the diagnosis window to scan the rest part of the image when the average value is larger than or equal to the preset threshold of the average value or the variance is larger than or equal to the preset threshold of the variance.
Advantageously, the fault diagnosis module is configured to, after each sliding of the diagnosis window, perform the following steps:
counting the number of abnormal pixel points in the current diagnosis window, and calculating the average value and the variance of Euclidean distances among all the abnormal pixel points;
if the number of the abnormal pixel points is larger than a preset threshold of the number of the abnormal pixel points, the average value is smaller than a preset threshold of the average value, and the variance is smaller than a preset threshold of the variance, generating image fault information;
and if the number of the abnormal pixel points is less than or equal to a preset threshold of the number of the abnormal pixel points, or the average value is greater than or equal to a preset threshold of the average value, or the variance is greater than or equal to a preset threshold of the variance, continuing to slide the diagnosis window to scan the rest part of the image.
The present disclosure also provides an electronic device comprising a processor and instructions stored thereon for execution by the processor, wherein the instructions, when executed by the processor, implement the steps of the method for diagnosing image faults according to any of the above embodiments.
The present disclosure also provides a storage medium storing an application program, which when executed by a processor implements the steps of the method for diagnosing image failure according to any one of the above embodiments.
The image fault diagnosis scheme is particularly suitable for deep learning of an automatic driving system. The inventor discovers that the automatic driving deep learning has higher fault tolerance on abnormal pixel points and only a specific dead pixel state can influence the accuracy of the visual deep learning based on analysis, thereby creating an evaluation standard and a method for image faults. The method can effectively reduce the calculated amount of the automatic driving system, improve the reliability of image diagnosis and further improve the automatic driving cruising performance of the vehicle.
Drawings
FIG. 1 is a flow chart of an exemplary method for diagnosing image faults.
FIG. 2 is a specific embodiment of the process shown in FIG. 1.
Fig. 3-1 through 3-3 are exemplary camera images to be diagnosed.
FIG. 4 is an exemplary system for diagnosing image faults.
Detailed Description
To further describe the relevant content of the present disclosure, the following description will be made in conjunction with a plurality of embodiments and accompanying drawings. The description relates only to a part of the embodiments and, on the basis of the described embodiments, a person skilled in the art will be able to conceive of other undescribed related or similar embodiments of the described embodiments without the need for inventive work.
FIG. 1 shows one example method for diagnosing camera image faults, which includes the steps of:
s101: the diagnostic window is slid to scan the image to be diagnosed.
In one or more embodiments, the diagnosis window is set to have a suitable length m and width n, the values of m and n being related to the accuracy of the image failure diagnosis, the smaller the value, the higher the accuracy, and the larger the calculation amount. For this reason, the minimum recognition area and/or the size of the image to be detected may be set depending on the machine depth learning. For example, the size of the diagnostic window is between the machine depth learning minimum identification region and the size of the image to be detected.
In addition, the setting of the diagnostic window may also include each sliding step of the diagnostic window, i.e., the distance moved by each sliding of the diagnostic window. For example, in one or more embodiments, the diagnostic window is slid only one pixel in one direction at a time to ensure that each pixel on the image is scanned.
The step of sliding the diagnosis window is mainly based on the set diagnosis window to scan the image to be diagnosed according to a preset sliding path, and a target area is selected for the subsequent diagnosis step. For example, the diagnostic window slides on the image in the horizontal direction according to the set step length, and after one line of scanning is completed, the diagnostic window slides in the vertical direction, that is, the image to be diagnosed is scanned line by line from left to right and from top to bottom until the whole image is traversed and no defect or fault is found.
S102: and carrying out image fault diagnosis after sliding the diagnosis window every time.
Specific contents of this step include, but are not limited to: and after the diagnosis window is slid according to a preset step length each time, judging whether an image fault exists according to the number of the abnormal pixel points in the current diagnosis window and the average value and the variance of Euclidean distances among all the abnormal pixel points. In other words, after each sliding of the diagnostic window is performed according to the preset step length, image fault diagnosis is performed, and the factors considered in the diagnostic process at least include one or more of the number of abnormal pixel points in the diagnostic window and the average value and variance of euclidean distances between all the abnormal pixel points.
In one or more embodiments, whether an abnormal pixel exists is determined according to the gray value of each pixel of the image. For example, the gray scale value range of the image may be set as the threshold range of the abnormal pixel according to the pixel characteristics of the specific camera, and when the gray scale value of the pixel is not within the threshold range, the pixel is determined to be the abnormal pixel.
The calculation method of the euclidean distance between the abnormal pixel points is as follows (formula 1, for example, accurate to three decimal places):
wherein the content of the first and second substances,
is a point
And point
The euclidean distance between them,jis as followsjThe number of the abnormal pixel points is one,kis as followskEach abnormal pixel point;
the calculation method of the average value of the euclidean distances is as follows (formula 2):
the calculation method of the variance of the euclidean distance is as follows (formula 3):
wherein the content of the first and second substances,i= 
，Nthe number of the abnormal pixel points is shown.
FIG. 2 illustrates a specific embodiment of the method shown in FIG. 1, which includes:
s1: a camera image acquired in real time is acquired.
S2: and sliding the diagnosis window by a preset step length according to a preset sliding path to scan the image. As described above, the sliding path of the diagnosis window is, for example, from left to right and from top to bottom, and the images to be diagnosed slide sequentially.
S3: and after the diagnosis window slides for a preset step length, counting the number of the abnormal pixel points in the current diagnosis window and the coordinates of each abnormal pixel point.
S4: and if the number of the abnormal pixel points is less than or equal to a preset threshold value, continuing to slide the diagnosis window to scan the rest parts of the image until the whole image is scanned to finish diagnosis of a single image, and starting acquisition and diagnosis of the next image. The preset threshold of the number of abnormal pixels can be set as required, wherein the larger the preset threshold of the number of abnormal pixels is, the lower the diagnosis precision is, and the smaller the calculation amount is.
S5: if the number of the abnormal pixel points is larger than a preset threshold value of the number of the abnormal pixel points, calculating the average value and the variance of Euclidean distances among all the abnormal pixel points;
s6: if the average value and the variance are both smaller than a preset threshold value, generating image fault information, indicating image errors, completing the diagnosis of the current image, and starting the acquisition and diagnosis of the next image; and if the average value and the variance are both larger than or equal to the preset threshold value, continuing to slide the diagnosis window to scan the rest part of the image until the whole image is scanned to finish diagnosis of a single image, and starting acquisition and diagnosis of the next image. The Euclidean distance average value preset threshold and the Euclidean distance variance preset threshold can also be set according to requirements, wherein the larger the threshold is, the higher the diagnosis precision is, and the smaller the calculated amount is.
The following will specifically describe the relevant contents of the above method by taking a plurality of images of the camera to be diagnosed shown in fig. 3-1, 3-2, and 3-3 as an example, wherein the black squares in the figures represent abnormal pixel points.
For the diagnosis method, for example, the width and length of the diagnosis window are set to 25 × 25 pixels, and a preset threshold a =3 of the number of abnormal pixel points is set, a preset threshold b =4 of the euclidean distance average of the abnormal pixel points, and a preset threshold c =3 of the euclidean distance variance.
Firstly, an image (as shown in fig. 3-1) is collected, the number of abnormal pixels in the image and the coordinates thereof are counted, wherein the number of the abnormal pixels is 2, and the coordinates are (5, 25) and (8, 19), respectively. Since the number of the abnormal pixel points in fig. 3-1 is less than the preset threshold value a =3, and the whole image is scanned, the image is determined to be free of defects, and the diagnosis of the current image is completed.
Then, the image is collected again (as shown in fig. 3-2), and the number of abnormal pixels and their coordinates in the image are counted, wherein the number of abnormal pixels is 4, and the coordinates are (6, 23), (20, 23), (8, 15), (21, 15), respectively. Since the number of the abnormal pixel points in fig. 3-2 is greater than the preset threshold value a =3, the euclidean distances between the abnormal pixel points are calculated according to the formula (1), and the obtained euclidean distances are respectively: 3.742, 8.246, 18.788, 14.422, 8.062, 13. Then, the Euclidean distance mean value is calculated as 11.043 according to the formula (2), and the Euclidean distance variance is calculated as 24.208 according to the formula (3). Since the average value of fig. 3-2 is greater than the preset value b =4, the variance is greater than the preset value c =3, and the entire picture is scanned, it is assumed that the image is defect-free.
And then, acquiring the image again (as shown in fig. 3-3), and counting the number of abnormal pixel points and the coordinates thereof in the image, wherein the number of the abnormal pixel points is 4, and the coordinates are (18, 13), (15, 13), (14, 17) and (15, 17), respectively. Since the number of the abnormal pixel points in fig. 3-3 is greater than the preset threshold value a =3, the euclidean distances between all the abnormal pixel points are calculated according to the formula (1), and the obtained euclidean distances are respectively: 3. 5.657, 5, 4.123, 4, 1. The mean value of euclidean distances calculated according to equation (2) is 3.797, and the variance of euclidean distances calculated according to equation (3) is 2.252. The mean of fig. 3-3 is less than the preset value b =4 and the variance of fig. 3-3 is less than the preset value c =3, indicating an image error, generating a malfunction alarm signal, reacquiring the image, and entering the next diagnostic cycle.
The above specific embodiments may have various modifications, including, for example:
s1: camera images are acquired in real time.
S2: the diagnostic window is slid by a predetermined step size to scan the acquired image according to a predetermined sliding path. As described above, the sliding path of the diagnosis window is, for example, from left to right and from top to bottom, and the images to be diagnosed slide sequentially.
S3: and after the diagnosis window is slid by a preset step length, counting the number of abnormal pixel points in the current diagnosis window and the coordinates of each abnormal pixel point, and calculating the average value and the variance of Euclidean distances among all the abnormal pixel points.
S4: if the number of the abnormal pixel points is larger than a preset threshold value and the average value and the variance are smaller than the preset threshold value, generating image fault information, completing diagnosis of the current image, and starting acquisition and diagnosis of the next image;
s5: if the number of the abnormal pixel points is smaller than or equal to a preset threshold, or the average value is larger than or equal to a preset threshold of the average value, or the variance is larger than or equal to a preset threshold of the variance, the diagnosis window is continuously slid to scan the rest parts of the image until the whole image is scanned to finish diagnosis of a single image, and acquisition and diagnosis of the next image are started.
Fig. 4 illustrates a system 10 for image fault diagnosis, comprising:
an image acquisition module 11 arranged to receive camera images acquired in real time;
an image scanning module 12 which is provided with a diagnosis window and scans the camera image by sliding the diagnosis window;
and the fault diagnosis module 13 is used for judging whether an image fault exists or not according to the number of the abnormal pixel points in the current diagnosis window and the average value and the variance of Euclidean distances among all the abnormal pixel points after the diagnosis window is slid every time.
For example, in one or more embodiments, the fault diagnosis module 13 is configured to:
after the diagnosis window is slid every time, counting the number of abnormal pixel points in the current diagnosis window;
if the number of the abnormal pixel points is less than or equal to the preset threshold of the number of the abnormal pixel points, continuously sliding the diagnosis window to scan the rest part of the image;
if the number of the abnormal pixel points is larger than a preset threshold of the number of the abnormal pixel points, calculating the average value and the variance of Euclidean distances among all the abnormal pixel points, generating image fault information when the average value is smaller than the preset threshold of the average value and the variance is smaller than a preset threshold of the variance, and continuously sliding the diagnosis window to scan the rest part of the image when the average value is larger than or equal to the preset threshold of the average value or the variance is larger than or equal to the preset threshold of the variance.
For another example, in one or more embodiments, the fault diagnosis module 13 is configured to, after sliding the diagnosis window each time, count the number of abnormal pixel points in the current diagnosis window, and calculate an average value and a variance of euclidean distances between all the abnormal pixel points;
if the number of the abnormal pixel points is larger than a preset threshold of the number of the abnormal pixel points, the average value is smaller than a preset threshold of the average value, and the variance is smaller than a preset threshold of the variance, generating image fault information;
and if the number of the abnormal pixel points is less than or equal to a preset threshold of the number of the abnormal pixel points, or the average value is greater than or equal to a preset threshold of the average value, or the variance is greater than or equal to a preset threshold of the variance, continuing to slide the diagnosis window to scan the rest part of the image.
It will be understood by those skilled in the art that all or part of the steps in the methods of the embodiments described above may be implemented by hardware related to instructions of a computer program, and the computer program may be stored in a non-volatile computer-readable storage medium, and when executed, the computer program may include the procedures of the embodiments of the methods described above, which are not described herein again.
The above examples are only intended to illustrate the technical solutions of the present disclosure, and not to limit them. It will be appreciated by those skilled in the art that modifications may be made to the teachings of the foregoing embodiments, or equivalents may be substituted for elements thereof without departing from the spirit or scope of the corresponding embodiments.
Claims (10)
1. A method of diagnosing image faults for autonomous driving, comprising:
sliding the diagnosis window to scan the image to be diagnosed;
and after the diagnosis window is slid every time, judging whether an image fault exists or not according to the number of the abnormal pixel points in the current diagnosis window and the average value and the variance of Euclidean distances among all the abnormal pixel points.
2. The method according to claim 1, characterized in that after each sliding of the diagnostic window, the following steps are performed:
counting the number of abnormal pixel points in the current diagnosis window;
if the number of the abnormal pixel points is less than or equal to the preset threshold of the number of the abnormal pixel points, continuously sliding the diagnosis window to scan the rest part of the image;
if the number of the abnormal pixel points is larger than a preset threshold of the number of the abnormal pixel points, calculating the average value and the variance of Euclidean distances among all the abnormal pixel points, generating image fault information when the average value is smaller than the preset threshold of the average value and the variance is smaller than a preset threshold of the variance, and continuously sliding the diagnosis window to scan the rest part of the image when the average value is larger than or equal to the preset threshold of the average value or the variance is larger than or equal to the preset threshold of the variance.
3. The method according to claim 1, characterized in that after each sliding of the diagnostic window, the following steps are performed:
counting the number of abnormal pixel points in the current diagnosis window, and calculating the average value and the variance of Euclidean distances among all the abnormal pixel points;
if the number of the abnormal pixel points is larger than a preset threshold of the number of the abnormal pixel points, the average value is smaller than a preset threshold of the average value, and the variance is smaller than a preset threshold of the variance, generating image fault information;
and if the number of the abnormal pixel points is less than or equal to a preset threshold of the number of the abnormal pixel points, or the average value is greater than or equal to a preset threshold of the average value, or the variance is greater than or equal to a preset threshold of the variance, continuing to slide the diagnosis window to scan the rest part of the image.
4. The method of any one of claims 1-3, wherein the length and width of the diagnostic window are set according to a deep-learning minimum identification zone.
5. The method according to any one of claims 1 to 3, wherein when the gray level value of a pixel point in the image is not within the gray level threshold range of the pixel point, the pixel point is determined to be an abnormal pixel point.
6. A system for diagnostic image faults for autonomous driving, comprising:
an image acquisition module arranged to receive images acquired in real time;
an image scanning module provided with a diagnosis window and scanning the image by sliding the diagnosis window;
and the fault diagnosis module is set to judge whether an image fault exists according to the number of the abnormal pixel points in the current diagnosis window and the average value and the variance of Euclidean distances among all the abnormal pixel points after the diagnosis window is slid every time.
7. The system of claim 6, wherein the fault diagnosis module is configured to perform the following diagnostic steps after each sliding of the diagnostic window:
counting the number of abnormal pixel points in the current diagnosis window;
if the number of the abnormal pixel points is less than or equal to the preset threshold of the number of the abnormal pixel points, continuously sliding the diagnosis window to scan the rest part of the image;
if the number of the abnormal pixel points is larger than a preset threshold of the number of the abnormal pixel points, calculating the average value and the variance of Euclidean distances among all the abnormal pixel points, generating image fault information when the average value is smaller than the preset threshold of the average value and the variance is smaller than a preset threshold of the variance, and continuously sliding the diagnosis window to scan the rest part of the image when the average value is larger than or equal to the preset threshold of the average value or the variance is larger than or equal to the preset threshold of the variance.
8. The system of claim 6, wherein the fault diagnosis module is configured to, after each sliding of the diagnosis window, perform the steps of:
counting the number of abnormal pixel points in the current diagnosis window, and calculating the average value and the variance of Euclidean distances among all the abnormal pixel points;
if the number of the abnormal pixel points is larger than a preset threshold of the number of the abnormal pixel points, the average value is smaller than a preset threshold of the average value, and the variance is smaller than a preset threshold of the variance, generating image fault information;
and if the number of the abnormal pixel points is less than or equal to a preset threshold of the number of the abnormal pixel points, or the average value is greater than or equal to a preset threshold of the average value, or the variance is greater than or equal to a preset threshold of the variance, continuing to slide the diagnosis window to scan the rest part of the image.
9. An electronic device, comprising:
a processor;
a memory storing instructions for execution by the processor;
wherein the instructions, when executed by the processor, implement the method of any of claims 1 to 5.
10. A storage medium, characterized in that the storage medium stores an application program which, when executed by a processor, implements the method according to any one of claims 1 to 5.
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| JAMES BURGER: "Bad Pixel Detection in Hyperspectral Staring Camera Systems", 《NIR NEWS》 * |
| 刘哲等: "智能识别视频图像质量分析系统在平安城市的应用", 《软件》 * |
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