Image overexposure recognition method, device and equipment and readable storage medium

Abstract

The invention discloses an image overexposure recognition method, device and equipment and a readable storage medium. The method comprises the steps that an image collected by a vehicle camera is acquired, the image is divided into a plurality of rectangular sub-images, a plurality of rectangular sub-images which are not overlapped with one another are determined from each sub-image, and pixel points at the upper left corner of each sub-image are not overlapped with pixel points at the upper left corner of the subimage; for each sub-sub-image, calculating an average brightness value of the sub-sub-image according to the integral image of the target pixel point corresponding to the sub-sub-image and the pixel coordinates of the left upper corner pixel point and the right lower corner pixel point of the sub-sub-image in the sub-image to which the sub-sub-image belongs; and for each sub-image, obtaining an overexposure identification result according to the average brightness value of all the sub-sub-images included in each sub-image. According to the method and the device, the average brightness value of all the sub-images can be quickly and accurately determined according to the integral image, so that the overexposure area in the image can be quickly and accurately identified.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, in particular to an image overexposure identification method, device and equipment and a computer readable storage medium. BACKGROUND

[0002] An assisted driving system dominated by a visual system needs to make driving decisions based on images collected by a vehicle camera. However, images are prone to overexposure due to environmental interference, which limits or invalidates visual perception algorithms, resulting in the assisted driving function failing to timely remind or degrade. Therefore, there is an urgent need for a method for identifying whether an image has an overexposure region. SUMMARY

[0003] To solve the above technical problems, the present application provides an image overexposure identification method, device, equipment and computer readable storage medium.

[0004] In a first aspect, the present application provides an image overexposure identification method, which comprises: obtaining an image collected by a vehicle camera, dividing the image into a plurality of rectangular sub-images and determining a plurality of rectangular and non-overlapping sub-sub-images from each sub-image, wherein the top-left pixel point of each sub-sub-image does not coincide with the top-left pixel point of the sub-image to which it belongs; for each sub-sub-image, calculating an average brightness value of the sub-sub-image according to an integral image of a target pixel point corresponding to the sub-sub-image and pixel coordinates of the top-left pixel point and the bottom-right pixel point of the sub-sub-image in the sub-image to which it belongs; for each sub-image, obtaining an overexposure identification result according to the average brightness values of all sub-sub-images included in the sub-image.

[0005] In combination with the first aspect, in an implementation manner, before the step of calculating the average brightness value of the sub-sub-image according to the integral image of the target pixel point corresponding to the sub-sub-image and the pixel coordinates of the top-left pixel point and the bottom-right pixel point of the sub-sub-image in the sub-image to which it belongs, the method further comprises: for each sub-image, calculating an integral image corresponding to each pixel coordinate in the sub-image, wherein the pixel coordinate system of the sub-image takes the top-left coordinate of the sub-image as the origin.

[0006] In combination with the first aspect, in an implementation manner, the pixel coordinates of the top-left pixel point and the bottom-right pixel point of the sub-sub-image in the sub-image to which it belongs are respectively denoted as (x0, y0) and (x1, y1), and the pixel coordinates of the pixel points included in the target pixel point in the sub-image to which it belongs are respectively denoted as (x0, y0), (x0, y1), (x1, y0) and (x1, y1). ​​​​, , , , , , , .

[0007] With reference to the first aspect, in an implementation form of the first aspect, the calculating the average brightness value of the sub-sub image according to the integral image of the target pixel point corresponding to the sub-sub image and the pixel coordinates of the top-left pixel point and the bottom-right pixel point of the sub-sub image in the sub image to which the sub-sub image belongs comprises: substituting the integral image of the target pixel point corresponding to the sub-sub image and the pixel coordinates of the top-left pixel point and the bottom-right pixel point of the sub-sub image in the sub image to which the sub-sub image belongs into an average brightness value calculation formula to obtain the average brightness value of the sub-sub image, the average brightness value calculation formula being:

[0008] wherein, represents the integral image of the pixel point with the pixel coordinates of, , represents the integral image of the pixel point with the pixel coordinates of, , represents the integral image of the pixel point with the pixel coordinates of, , represents the integral image of the pixel point with the pixel coordinates of, , represents the integral image of the pixel point with the pixel coordinates of, , represents the integral image of the pixel point with the pixel coordinates of.

[0009] With reference to the first aspect, in an implementation form of the first aspect, the obtaining the overexposure identification result for each sub image according to the average brightness values of all the sub-sub images included in the sub image comprises: comparing the average brightness values of all the sub-sub images included in each sub image with a preset brightness value respectively; if the number of the sub-sub images with the average brightness value greater than the preset brightness value is greater than a preset value, determining that the sub image is overexposed.

[0010] In a second aspect, an image overexposure identification device is provided, comprising: a division module configured to acquire an image captured by a vehicle camera, divide the image into a plurality of rectangular sub images, and determine a plurality of rectangular and non-overlapping sub-sub images from each sub image, wherein the top-left pixel point of each sub-sub image is not coincident with the top-left pixel point of the sub image to which the sub-sub image belongs; ​The brightness calculation module is used to calculate the average brightness value of each sub-sub-image based on the integral image of the target pixel corresponding to the sub-sub-image and the pixel coordinates of the top left and bottom right pixels of the sub-sub-image in its respective sub-image. The recognition module is used to obtain overexposure recognition results for each sub-image based on the average brightness value of all included sub-sub-images.

[0011] In conjunction with the second aspect, in one embodiment, the image overexposure recognition device further includes an integral calculation module, used for: For each sub-image, calculate the integral image corresponding to each pixel coordinate in the sub-image, where the pixel coordinate system of the sub-image takes the top-left corner of the sub-image as the origin.

[0012] In conjunction with the second aspect, in one embodiment, the pixel coordinates of the top-left and bottom-right pixels of the sub-image within their respective sub-images are respectively denoted as (…). , )and( , The pixel coordinates of the target pixel in its sub-image are as follows: , ), ( , ), ( , )as well as( , ).

[0013] Thirdly, embodiments of this application provide an image overexposure recognition device, which includes a processor, a memory, and an image overexposure recognition program stored in the memory and executable by the processor. When the image overexposure recognition program is executed by the processor, it implements the steps of the image overexposure recognition method as described in the first aspect.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing an image overexposure recognition program, wherein when the image overexposure recognition program is executed by a processor, it implements the steps of the image overexposure recognition method as described in the first aspect.

[0015] The beneficial effects of the technical solutions provided in this application include: In this embodiment, an image captured by a vehicle camera is acquired, and the image is divided into several rectangular sub-images. From each sub-image, several non-overlapping rectangular sub-sub-images are determined, wherein the top-left corner pixel of each sub-sub-image does not overlap with the top-left corner pixel of its parent sub-image. For each sub-sub-image, the average brightness value of the sub-sub-image is calculated based on the integral image of the target pixel corresponding to the sub-image and the pixel coordinates of the top-left and bottom-right corner pixels of the sub-image within its parent sub-image. For each sub-image, an overexposure identification result is obtained based on the average brightness value of all included sub-sub-images. Through this embodiment, the average brightness value of all sub-sub-images can be quickly and accurately determined based on the integral image, thereby achieving rapid and accurate identification of overexposure areas in the image. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an embodiment of the image exposure anomaly recognition method of this application; Figure 2 This is a schematic diagram of image segmentation in one embodiment of the image exposure anomaly recognition method of this application; Figure 3 This is a schematic diagram of sub-image segmentation in one embodiment of the image exposure anomaly recognition method of this application; Figure 4 This is a schematic diagram of the functional modules of an embodiment of the image exposure anomaly recognition device of this application; Figure 5 This is a schematic diagram of the hardware structure of the image exposure anomaly recognition device involved in the embodiments of this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0019] In a first aspect, embodiments of this application provide a method for identifying image exposure anomalies.

[0020] In one embodiment, reference is made to Figure 1 , Figure 1 This is a schematic flowchart of an embodiment of the image exposure anomaly recognition method of this application. Figure 1As shown, the image exposure anomaly identification method includes: Step S10: Acquire the image captured by the vehicle camera, divide the image into several rectangular sub-images, and determine several rectangular non-overlapping sub-sub-images from each sub-image, wherein the upper left corner pixel of each sub-sub-image does not overlap with the upper left corner pixel of its sub-image. In this embodiment, images captured by vehicle cameras are divided according to different rules in different scenarios.

[0021] Taking a road driving scenario as an example, the system first checks whether the lane lines in the most recently captured frame of the image are normal. This is done based on the confidence level output by the lane line detection algorithm; if the confidence threshold is met, the image is considered normal. If not, lane line data determined from the previous frame is used as a replacement. If normal, the region of interest (ROI) is extracted based on the lane lines in the image, and the corresponding image is divided into several rectangular sub-images. (Refer to...) Figure 2 , Figure 2 This is a schematic diagram of image segmentation in one embodiment of the image exposure anomaly recognition method of this application. For example... Figure 2 As shown, based on lane lines ( Figure 2 The image is divided into sub-images M1, M2, and M3 by the dashed lines (in the diagram). Further, several rectangular, non-overlapping sub-sub-images are determined from each sub-image. (Refer to...) Figure 3 , Figure 3 This is a schematic diagram of sub-image segmentation in one embodiment of the image exposure anomaly recognition method of this application. Figure 3 As shown, sub-sub-images N1 to N9 were determined from sub-image M3. Similar processing was performed on the other sub-images, which will not be elaborated upon here.

[0022] It should be noted that the sub-image division needs to meet the following constraints: the top left corner pixel of each sub-image does not coincide with the top left corner pixel of its parent sub-image, the sub-images are rectangular and no two sub-images overlap.

[0023] Step S20: For each sub-image, calculate the average brightness value of the sub-image based on the integral image of the target pixel corresponding to the sub-image and the pixel coordinates of the top left and bottom right pixels of the sub-image in the sub-image. In this embodiment, the target pixel corresponding to the sub-image is located in the sub-image to which the sub-image belongs. The integral image corresponding to each pixel position in the sub-image can be pre-calculated, so as to quickly find the integral image of the target pixel from the pre-calculated results.

[0024] Furthermore, in one embodiment, before step S20, the method further includes: For each sub-image, calculate the integral image corresponding to each pixel coordinate in the sub-image, where the pixel coordinate system of the sub-image takes the top-left corner of the sub-image as the origin.

[0025] In this embodiment, taking sub-image M3 as an example, the integral image corresponding to each pixel coordinate in sub-image M3 is calculated using the following formula:

[0026] in, pixel coordinates ( , The corresponding integral image, pixel coordinates ( The pixel value at (). It should be noted that the pixel coordinates of each pixel in each sub-image are determined based on the coordinate system corresponding to the sub-image, and the origin of the coordinate system corresponding to each sub-image is the top-left pixel of each sub-image.

[0027] Further, in one embodiment, the pixel coordinates of the top-left and bottom-right pixels of the sub-image in their respective sub-images are denoted as (…). , )and( , The pixel coordinates of the target pixel in its sub-image are as follows: , ), ( , ), ( , )as well as( , ).

[0028] Further, in one embodiment, step S20 includes: The average brightness value of the sub-image is obtained by substituting the integral image of the target pixel corresponding to the sub-image, as well as the pixel coordinates of the top-left and bottom-right pixels of the sub-image within its respective sub-image, into the average brightness value calculation formula. The average brightness value calculation formula is as follows:

[0029] in, The pixel coordinates are ( , The integral image of the pixels, The pixel coordinates are ( , The integral image of the pixels, The pixel coordinates are ( , The integral image of the pixels, The pixel coordinates are ( , An integral image of pixels.

[0030] In this embodiment, taking the calculation of the average brightness value of N1 as an example, it is assumed that the pixel coordinates of the upper left and lower right pixels of N1 in the sub-image M3 are denoted as ( , )and( , ),in,( , ) = (5, 2), , If (10, 5) = (10, 5), then the target pixels include: the pixel at coordinates (10, 5), the pixel at coordinates (4, 5), the pixel at coordinates (10, 1), and the pixel at coordinates (4, 1). Therefore, the target pixels are obtained as follows: , , ,as well as Substituting these values ​​into the formula for calculating the average brightness value, we obtain the average brightness value of N1. for:

[0031] By following this method, the average brightness value of each sub-image can be obtained.

[0032] Step S30: For each sub-image, obtain the overexposure identification result based on the average brightness value of all included sub-sub-images.

[0033] In this embodiment, taking sub-image M3 as an example, the average brightness values ​​of the nine sub-images N1 to N9 are obtained. The average brightness value of each sub-image can be compared with the preset brightness value to obtain the overall exposure of sub-image M3, thereby obtaining the overexposure recognition result of sub-image M3.

[0034] Further, in one embodiment, step S30 includes: For each sub-image, the average brightness value of all included sub-sub-images is compared with a preset brightness value; if the number of sub-sub-images with an average brightness value greater than the preset brightness value is greater than the preset value, then the sub-image is determined to be overexposed.

[0035] In this embodiment, the preset brightness value is set according to actual needs and is not limited here. Taking M3 as an example, by comparison, the number X of sub-images with an average brightness value greater than the preset brightness value can be determined. If X is greater than the preset value, then sub-image M3 is determined to be overexposed. The preset value is set according to actual needs. For example, when determining whether sub-image M3 is overexposed, the preset value is set to half of the total number of sub-images included in sub-image M3.

[0036] In this embodiment, an image captured by a vehicle camera is acquired, and the image is divided into several rectangular sub-images. From each sub-image, several non-overlapping rectangular sub-sub-images are determined, wherein the top-left corner pixel of each sub-sub-image does not overlap with the top-left corner pixel of its parent sub-image. For each sub-sub-image, the average brightness value of the sub-sub-image is calculated based on the integral image of the target pixel corresponding to the sub-image and the pixel coordinates of the top-left and bottom-right corner pixels of the sub-image within its parent sub-image. For each sub-image, an overexposure identification result is obtained based on the average brightness value of all included sub-sub-images. Through this embodiment, the average brightness value of all sub-sub-images can be quickly and accurately determined based on the integral image, thereby achieving rapid and accurate identification of overexposure areas in the image.

[0037] Secondly, embodiments of this application also provide an image exposure anomaly recognition device.

[0038] In one embodiment, reference is made to Figure 4 , Figure 4 This is a schematic diagram of the functional modules of an embodiment of the image exposure anomaly recognition device of this application. Figure 4 As shown, the image exposure anomaly recognition device includes: The segmentation module 10 is used to acquire images captured by the vehicle camera, divide the images into several rectangular sub-images, and determine several rectangular non-overlapping sub-sub-images from each sub-image, wherein the upper left corner pixel of each sub-sub-image does not overlap with the upper left corner pixel of its parent sub-image. The brightness calculation module 20 is used to calculate the average brightness value of each sub-sub-image based on the integral image of the target pixel corresponding to the sub-sub-image and the pixel coordinates of the upper left and lower right pixels of the sub-sub-image in the sub-image. The recognition module 30 is used to obtain an overexposure recognition result for each sub-image based on the average brightness value of all included sub-images.

[0039] Furthermore, in one embodiment, the image overexposure recognition device further includes an integration calculation module, used for: For each sub-image, calculate the integral image corresponding to each pixel coordinate in the sub-image, where the pixel coordinate system of the sub-image takes the top-left corner of the sub-image as the origin.

[0040] Further, in one embodiment, the pixel coordinates of the top-left and bottom-right pixels of the sub-image in their respective sub-images are denoted as (…). , )and( , The pixel coordinates of the target pixel in its sub-image are as follows: , ), ( , ), ( , )as well as( , ).

[0041] Furthermore, in one embodiment, the brightness calculation module 20 is used for: The average brightness value of the sub-image is obtained by substituting the integral image of the target pixel corresponding to the sub-image, as well as the pixel coordinates of the top-left and bottom-right pixels of the sub-image within its respective sub-image, into the average brightness value calculation formula. The average brightness value calculation formula is as follows:

[0042] in, The pixel coordinates are ( , The integral image of the pixels, The pixel coordinates are ( , The integral image of the pixels, The pixel coordinates are ( , The integral image of the pixels, The pixel coordinates are ( , An integral image of pixels.

[0043] Furthermore, in one embodiment, the identification module 30 is used for: For each sub-image, the average brightness value of all included sub-sub-images is compared with a preset brightness value. If the number of sub-images with an average brightness value greater than the preset brightness value is greater than the preset value, then the sub-image is determined to be overexposed.

[0044] The functions of each module in the above-mentioned image exposure anomaly recognition device correspond to the steps in the above-mentioned image exposure anomaly recognition method embodiment, and their functions and implementation processes will not be described in detail here.

[0045] Thirdly, embodiments of this application provide an image exposure anomaly recognition device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0046] Reference Figure 5 , Figure 5 This is a schematic diagram of the hardware structure of the image exposure anomaly recognition device involved in the embodiments of this application. In the embodiments of this application, the image exposure anomaly recognition device may include a processor, a memory, a communication interface, and a communication bus.

[0047] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0048] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the image exposure anomaly recognition device, as well as interfaces used for interconnecting the image exposure anomaly recognition device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0049] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0050] The processor can be a general-purpose processor, which can call the image exposure anomaly recognition program stored in memory and execute the image exposure anomaly recognition method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the image exposure anomaly recognition program is called can be referred to in various embodiments of the image exposure anomaly recognition method of this application, and will not be repeated here.

[0051] Those skilled in the art will understand that Figure 5 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0052] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0053] The present application has a computer-readable storage medium storing an image exposure anomaly recognition program, wherein when the image exposure anomaly recognition program is executed by a processor, it implements the steps of the image exposure anomaly recognition method as described above.

[0054] The method implemented when the image exposure anomaly recognition program is executed can be referred to in various embodiments of the image exposure anomaly recognition method of this application, and will not be repeated here.

[0055] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0056] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0057] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0058] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0059] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0060] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0061] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. An image overexposure recognition method, characterized by, The image overexposure identification method comprises: An image captured by a vehicle camera is obtained, the image is divided into a plurality of rectangular sub-images, and a plurality of rectangular and non-overlapping sub-sub-images are determined from each sub-image, wherein the upper-left corner pixel point of each sub-sub-image does not coincide with the upper-left corner pixel point of the sub-image to which the sub-sub-image belongs; For each sub-sub-image, the average brightness value of the sub-sub-image is calculated according to the integral image of the target pixel point corresponding to the sub-sub-image and the pixel coordinates of the upper-left corner pixel point and the lower-right corner pixel point of the sub-sub-image in the sub-image to which the sub-sub-image belongs; For each sub-image, an overexposure identification result is obtained according to the average brightness values of all the sub-sub-images included in the sub-image.

2. The image overexposure recognition method of claim 1, wherein, Before the step of calculating, for each sub-sub-image, the average brightness value of the sub-sub-image according to the integral image of the target pixel point corresponding to the sub-sub-image and the pixel coordinates of the upper-left corner pixel point and the lower-right corner pixel point of the sub-sub-image in the sub-image to which the sub-sub-image belongs, the method further comprises: For each sub-image, the integral image corresponding to each pixel coordinate in the sub-image is calculated, wherein the pixel coordinate system of the sub-image takes the upper-left corner coordinate of the sub-image as the origin.

3. The image overexposure recognition method of claim 2, wherein, The pixel coordinates of the upper-left pixel point and the lower-right pixel point of the sub-sub image in the sub image are respectively denoted as (x0, y0) and (x1, y1) , ) and (x1, y1) , ), the pixel coordinates of the pixel points included in the target pixel point in the sub image are respectively (x0, y0), (x0, y1), (x1, y0), (x1, y1), (x0, y0) and (x1, y1) , ) (x1, y0) , ) (x1, y1) , ) (x0, y0) , ).

4. The image overexposure recognition method according to claim 3, characterized by, The step of calculating, for each sub-sub-image, the average brightness value of the sub-sub-image according to the integral image of the target pixel point corresponding to the sub-sub-image and the pixel coordinates of the upper-left corner pixel point and the lower-right corner pixel point of the sub-sub-image in the sub-image to which the sub-sub-image belongs comprises: The integral image of the target pixel point corresponding to the sub-sub-image and the pixel coordinates of the upper-left corner pixel point and the lower-right corner pixel point of the sub-sub-image in the sub-image to which the sub-sub-image belongs are substituted into an average brightness value calculation formula to obtain the average brightness value of the sub-sub-image, the average brightness value calculation formula being: wherein, represents an integral image of pixel points with pixel coordinates (x, y), , , represents an integral image of pixel points with pixel coordinates (x, y), , , represents an integral image of pixel points with pixel coordinates (x, y), , , represents an integral image of pixel points with pixel coordinates (x, y), , .

5. The image overexposure recognition method of claim 1, wherein, The step of obtaining, for each sub-image, an overexposure identification result according to the average brightness values of all the sub-sub-images included in the sub-image comprises: For each sub-image, the average brightness values of all the sub-sub-images included in the sub-image are compared with a preset brightness value respectively; If the number of sub-sub-images with an average brightness value greater than the preset brightness value is greater than a preset value, it is determined that the sub-image is overexposed.

6. An image overexposure recognition apparatus characterized by comprising: The image overexposure identification device comprises: A division module is configured to obtain an image captured by a vehicle camera, divide the image into a plurality of rectangular sub-images, and determine a plurality of rectangular and non-overlapping sub-sub-images from each sub-image, wherein the upper-left corner pixel point of each sub-sub-image does not coincide with the upper-left corner pixel point of the sub-image to which the sub-sub-image belongs; A brightness calculation module is configured to calculate, for each sub-sub-image, the average brightness value of the sub-sub-image according to the integral image of the target pixel point corresponding to the sub-sub-image and the pixel coordinates of the upper-left corner pixel point and the lower-right corner pixel point of the sub-sub-image in the sub-image to which the sub-sub-image belongs; An identification module is configured to obtain, for each sub-image, an overexposure identification result according to the average brightness values of all the sub-sub-images included in the sub-image.

7. The image overexposure recognition apparatus as claimed in claim 6, wherein The image overexposure identification device further comprises an integral calculation module configured to: For each sub-image, the integral image corresponding to each pixel coordinate in the sub-image is calculated, wherein the pixel coordinate system of the sub-image takes the upper-left corner coordinate of the sub-image as the origin.

8. The image overexposure recognition apparatus as claimed in claim 7, wherein The pixel coordinates of the upper left pixel point and the lower right pixel point of the sub-sub image in the sub image are respectively denoted as (x0, y0) and (x1, y1) , ) and (x1, y1) , ), the pixel coordinates of the pixel points included in the target pixel point in the sub image are respectively (x0, y0), (x0, y1), (x1, y0), (x1, y1) , ), (x0, y0), (x1, y1) , ), (x0, y1), (x1, y0) , ) and (x0, y0), (x1, y1) , ).

9. An image overexposure recognition device characterized by comprising: The image overexposure identification device includes a processor, a memory, and an image overexposure identification program stored on the memory and executable by the processor, wherein the image overexposure identification program, when executed by the processor, implements the steps of the image overexposure identification method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium has stored thereon an image overexposure identification program, wherein the image overexposure identification program, when executed by a processor, implements the steps of the image overexposure identification method according to any one of claims 1 to 5.

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