Automatic test method, device and equipment for image sensor production and storage medium

Abstract

The invention discloses an automatic test method, device and equipment for image sensor production and a storage medium. The method comprises the following steps: acquiring an image picture acquired by an image sensor; determining dead pixels in the image picture; determining a dead pixel cluster of the image picture according to the dead pixels in the image picture; and judging whether the image output picture of the image sensor is normal or not according to the dead pixels and the dead pixel clusters. According to the method and the device, whether the image output picture is normal or not is automatically judged according to the dead pixels and the dead pixel cluster parameters in the image picture by utilizing the automatically collected image picture, manual judgment is not needed, misjudgment and missed judgment are avoided, and meanwhile, the test efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image sensors, and more specifically to an automatic testing method, device, equipment and storage medium for producing image sensors. Background Art

[0002] At present, there are more and more electronic products with image sensors, which are used to collect images for display or storage. Especially in the fields of photography, videography, and monitoring, especially security monitoring IPC, image sensors are a very core module. These products need to be tested in the production process to see whether the functional modules of the image sensors are working properly, whether the photosensitivity imaging is normal, whether there are bright spots or dark spots on the screen, and other issues that do not meet product requirements.

[0003] At present, the traditional testing method is to assemble the image sensor module into a complete machine, and then have the worker visually check whether the display screen is lit normally after imaging. The human eye checks whether there are abnormalities such as bright spots, dark spots, black lines, white lines, etc., and finally judges whether the image sensor has bad spots. This method is inefficient and has high labor costs. At the same time, manual judgment is subjective, and there are prominent management problems such as employee missed tests and failure to automatically record. This will lead to the leakage of poor function of image sensors of some products, and ultimately lead to quality problems such as poor image quality not being discovered until the user uses the product. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide an automatic testing method, device, equipment and storage medium for producing an image sensor.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides an automatic test method for producing an image sensor, comprising:

[0007] Acquire the image captured by the image sensor;

[0008] Determine the bad pixels in the image;

[0009] Determine the bad pixel cluster of the image according to the bad pixels in the image;

[0010] Whether the image output of the image sensor is normal is determined based on the bad pixels and bad pixel clusters.

[0011] Furthermore, the step of acquiring the image captured by the image sensor includes:

[0012] Acquire an image captured by an image sensor in a black field environment;

[0013] Get the image picture collected by the image sensor in a white field environment.

[0014] Further, the bad pixels include dark pixels and bright pixels, and the step of determining the bad pixels in the image includes:

[0015] Calculate the pixel value of the target pixel;

[0016] Calculate the average pixel value of the surrounding pixels of the target pixel;

[0017] If the pixel value of the target pixel is less than the average pixel value of the surrounding pixels, the target pixel is determined to be a dark point;

[0018] If the pixel value of the target pixel is greater than the average pixel value of the surrounding pixels, the target pixel is determined to be a bright spot.

[0019] Further, the determining of the bad pixel clusters in the image according to the bad pixels in the image includes:

[0020] Calculate whether the number of consecutive bad pixels in the image is greater than or equal to a set bad pixel threshold;

[0021] If so, the pixel area formed by the continuous bad pixels is determined as a bad pixel cluster.

[0022] Furthermore, the image screen is divided into a first area and a second area, and judging whether the image output screen of the image sensor is normal according to the bad pixels and the bad pixel clusters includes:

[0023] Calculating the number of bad pixels and the number of bad pixel clusters in the first area and the second area respectively;

[0024] If the number of bad pixels and the number of bad pixel clusters in the first area are both less than the set first standard threshold, and the number of bad pixels and the number of bad pixel clusters in the second area are both less than the set second standard threshold, it is determined that the photosensitivity imaging of the image sensor is normal.

[0025] Furthermore, the first area is a central area of ​​the image screen, and the second area is a peripheral area of ​​the image screen, and the peripheral area is located outside the central area.

[0026] Furthermore, the area of ​​the peripheral region is larger than the area of ​​the central region.

[0027] In a second aspect, the present invention also provides an automatic testing device for producing an image sensor, comprising:

[0028] An acquisition unit, used to acquire an image captured by an image sensor;

[0029] A bad pixel determination unit, used to determine bad pixels in the image;

[0030] A bad pixel cluster determination unit, used to determine the bad pixel clusters of the image according to the bad pixels in the image;

[0031] The judging unit is used to judge whether the image output picture of the image sensor is normal according to the bad pixels and the bad pixel clusters.

[0032] In a third aspect, the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned automatic testing method for image sensor production when executing the computer program.

[0033] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the automatic testing method for image sensor production as described above.

[0034] Compared with the prior art, the present invention has the following beneficial effects: the automatic test method for image sensor production provided by the present invention includes acquiring an image screen captured by an image sensor; determining bad pixels in the image screen; determining bad pixel clusters in the image screen according to the bad pixels in the image screen; and judging whether the image output screen of the image sensor is normal according to the bad pixels and bad pixel clusters. The present invention utilizes the automatically captured image screen and automatically judges whether the image output screen is normal according to the bad pixels and bad pixel cluster parameters in the image screen, without the need for manual judgment, thus avoiding misjudgment and missed judgment, and improving the test efficiency at the same time.

[0035] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0037] Figure 1 A flowchart of an automatic testing method for image sensor production provided by a specific embodiment of the present invention;

[0038] Figure 2 A schematic block diagram of an automatic testing device for image sensor production provided by a specific embodiment of the present invention;

[0039] Figure 3A schematic block diagram of a computer device provided for a specific embodiment of the present invention;

[0040] Figure 4 A schematic diagram of the area division of an image screen provided by a specific embodiment of the present invention;

[0041] Figure 5 A schematic diagram of a bad pixel in an image provided by a specific embodiment of the present invention;

[0042] Figure 6 A schematic diagram of a bad pixel cluster in an image provided by a specific embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0045] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0046] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0047] like Figure 1 As shown, an embodiment of the present invention provides an automatic testing method for image sensor production, including the following steps: S10-S40.

[0048] S10, acquiring an image captured by an image sensor.

[0049] Before the test, the image sensor to be tested is placed inside a closed light box, which can provide scenes of darkness and different brightness. Then an image acquisition module is directly connected through wiring or using ejector pins. The image acquisition module can drive the image sensor to light up, and then cooperate with the control light box to turn off the light to capture a completely black image (i.e., a black field image), or by controlling the brightness of the light box, capture white field images at different brightness.

[0050] In one embodiment, step S10 specifically includes the following steps: S101 - S102 .

[0051] S101 , obtaining an image frame captured by an image sensor in a black field environment.

[0052] The image sensor acquires images in a completely dark environment and obtains a black field image. The black field image is used to test the performance of the image sensor in a dark environment.

[0053] A black field environment refers to a completely lightless test environment, which aims to simulate the working state of the image sensor in complete darkness. This environment is achieved by controlling the light box to turn off the lights, ensuring that the image sensor can capture images under no light conditions.

[0054] S102, acquiring an image frame captured by an image sensor in a white field environment.

[0055] The image sensor acquires images under bright lighting conditions and obtains a white field image. The white field image is used to detect the performance of the image sensor under lighting conditions.

[0056] The white field environment refers to a uniform and bright test environment that simulates the working state of the image sensor under sufficient light. This environment adjusts the white light provided by the light box to ensure the uniformity and appropriate brightness of the light source, so that the image sensor can capture images under good lighting conditions.

[0057] S20: Determine the bad pixels in the image.

[0058] Bad pixels include dark pixels and bright pixels. It should be noted that in the image captured in a black field environment, dark pixels cannot be found because the image itself is black, so only bright pixels can be found. In the image captured in a white field environment, both dark pixels and bright pixels can be found because the image itself has a certain brightness. If a pixel that should have a certain brightness appears black, then the pixel is a dark pixel. If the brightness of a pixel exceeds the brightness that it should have, then the pixel is a bright pixel.

[0059] In one embodiment, step S20 specifically includes the following steps: S201 - S204 .

[0060] S201, calculating the pixel value of the target pixel.

[0061] In the acquired image, a target pixel is selected (the target pixel can be any pixel in the image), and the RGB pixel value of the pixel is calculated. The specific calculation method can be to read the red (R), green (G), and blue (B) values ​​of each pixel, and then combine these three values ​​for calculation.

[0062] S202: Calculate the average pixel value of the surrounding pixels of the target pixel.

[0063] Select several pixels around the target pixel, which can be a 3x3 or 5x5 pixel area centered on the target pixel. Calculate the average pixel value of all pixels in the pixel area except the target pixel. This average pixel value is used as a reference value for the target pixel.

[0064] like Figure 5 As shown, in this embodiment, Figure 5 The number 100 is the target pixel, and the other eight pixels are the surrounding pixels. After calculating the pixel values ​​of these eight pixels respectively, the average pixel value of the eight pixels can be calculated.

[0065] S203: If the pixel value of the target pixel is less than the average pixel value of the surrounding pixels, the target pixel is determined to be a dark point.

[0066] In this embodiment, if Figure 5 If the pixel value of the target pixel point 100 is less than the average pixel value of the eight surrounding pixels, the target pixel point 100 is determined to be a dark point.

[0067] S204: If the pixel value of the target pixel is greater than the average pixel value of the surrounding pixels, the target pixel is determined to be a bright spot.

[0068] In this embodiment, if Figure 5 If the pixel value of the target pixel point 100 is smaller than the average pixel value of the eight surrounding pixels, the target pixel point 100 is determined to be a bright spot.

[0069] For steps S201-S204, by comparing pixel values, dark spots and bright spots in the image can be accurately located. By clear judgment criteria, subjective errors are reduced and the reliability of bad pixel detection is improved.

[0070] S30, determining bad pixel clusters in the image according to the bad pixels in the image.

[0071] A bad pixel cluster can be formed by a combination of multiple continuous dark pixels or multiple continuous bright pixels.

[0072] In one embodiment, step S30 specifically includes the following steps: S301 - S302 .

[0073] S301: Calculate whether the number of consecutive bad pixels in an image is greater than or equal to a set bad pixel threshold.

[0074] S302: If yes, the pixel area formed by the continuous bad pixels is determined as a bad pixel cluster.

[0075] For steps S301-S302, Figure 6 As shown, in this embodiment, the bad pixel threshold is three, Figure 6 In the figure, the number of consecutive bad pixels is three (i.e., three consecutive green squares in the figure), then these three consecutive bad pixels constitute a bad pixel cluster.

[0076] It should be noted that, in addition to the requirements for bad pixels, the evaluation of the image output of the image sensor also requires bad pixel clusters. This is because if the image has only a single bad pixel, the final product image can be corrected. However, if there are not many bad pixels and they are all concentrated together, the final product image cannot be corrected.

[0077] S40: judging whether the image output of the image sensor is normal according to the bad pixels and the bad pixel clusters.

[0078] For different image regions, the requirements for the number of bad pixels and bad pixel clusters may be different in strictness, that is, the requirements for some regions may be stricter, and for other regions may be appropriately relaxed as long as the set standards are met.

[0079] like Figure 4 As shown, in this embodiment, the image screen is divided into a first area and a second area, the first area is the central area of ​​the image screen, and the second area is the peripheral area of ​​the image screen. The peripheral area is located outside the central area, and the area of ​​the peripheral area is larger than that of the central area. Figure 4 The red area in the center is the red area, and the rest is the peripheral area. The length of the central area is half of the length of the entire image screen, and the height of the central area is also half of the height of the entire image screen.

[0080] In one embodiment, step S40 specifically includes the following steps: S401 - S402 .

[0081] S401, respectively calculating the number of bad pixels and the number of bad pixel clusters in the first area and the second area;

[0082] S402: If the number of bad pixels and the number of bad pixel clusters in the first area are both less than the set first standard threshold, and the number of bad pixels and the number of bad pixel clusters in the second area are both less than the set second standard threshold, it is determined that the photosensitivity imaging of the image sensor is normal.

[0083] For steps S401-S402, Figure 4 As shown, the requirements for the number of bad pixels and bad pixel clusters in the central area are more stringent than those in the peripheral area.

[0084] Assume that 3 bad pixels and 1 bad pixel cluster are detected in the first area, and 5 bad pixels and 2 bad pixel clusters are detected in the second area; set the first standard threshold of the number of bad pixels in the first area to 5, and the first standard threshold of the number of bad pixel clusters to 2. Set the second standard threshold of the number of bad pixels in the second area to 10, and the second standard threshold of the number of bad pixel clusters to 4. Since the number of bad pixels and the number of bad pixel clusters in the first area and the second area are both less than the corresponding thresholds, it is determined that the light sensing imaging of the image sensor is normal.

[0085] The present invention utilizes automatically collected image pictures and automatically determines whether the image output picture is normal according to the bad pixels and bad pixel cluster parameters in the image pictures, without the need for manual judgment, thus avoiding misjudgment and missed judgment and improving the test efficiency.

[0086] The embodiment of the present invention also provides an automatic test device for producing an image sensor, which is used to perform the steps in any embodiment of the automatic test method for producing an image sensor. Figure 2 , Figure 2 The schematic block diagram of the image sensor production automatic test device 100 provided in the embodiment of the present application is shown. The image sensor production automatic test device 100 specifically includes:

[0087] The acquisition unit 110 is used to acquire an image captured by the image sensor. The bad pixel determination unit 120 is used to determine the bad pixels in the image. The bad pixel cluster determination unit 130 is used to determine the bad pixel clusters in the image according to the bad pixels in the image. The judgment unit 140 is used to judge whether the image output of the image sensor is normal according to the bad pixels and the bad pixel clusters.

[0088] In one embodiment, the acquisition unit 110 includes:

[0089] The first acquisition module is used to acquire the image picture captured by the image sensor in a black field environment; the second acquisition module is used to acquire the image picture captured by the image sensor in a white field environment.

[0090] In one embodiment, the bad pixels include dark pixels and bright pixels, and the bad pixel determination unit 120 includes:

[0091] The first calculation module is used to calculate the pixel value of the target pixel; the second calculation module is used to calculate the average pixel value of the surrounding pixels of the target pixel; the first judgment module is used to judge that the target pixel is a dark spot if the pixel value of the target pixel is less than the average pixel value of the surrounding pixels; the second judgment module is used to judge that the target pixel is a bright spot if the pixel value of the target pixel is greater than the average pixel value of the surrounding pixels.

[0092] In one embodiment, the bad pixel cluster determination unit 130 includes:

[0093] The third calculation module is used to calculate whether the number of continuous bad pixels in the image is greater than or equal to a set bad pixel threshold; the determination unit is used to determine the pixel area formed by the continuous bad pixels as a bad pixel cluster.

[0094] In one embodiment, the image frame is divided into a first area and a second area, and the segmentation unit 140 includes:

[0095] The fourth calculation module is used to calculate the number of bad pixels and the number of bad pixel clusters in the first area and the second area respectively; the third judgment module is used to judge that the photosensitivity imaging of the image sensor is normal if the number of bad pixels and the number of bad pixel clusters in the first area are both less than the set first standard threshold, and the number of bad pixels and the number of bad pixel clusters in the second area are both less than the set second standard threshold.

[0096] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned image sensor production automatic test device 100 and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and brevity of description, it will not be repeated here.

[0097] The above-mentioned image sensor production automatic test device can be implemented in the form of a computer program. The computer program can be used in Figure 3 Runs on the computer device shown.

[0098] See also Figure 3 , Figure 3 700 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 700 may be a server, wherein the server may be an independent server or a server cluster composed of multiple servers.

[0099] like Figure 3 As shown, the computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the automatic test method for producing an image sensor as described above are implemented.

[0100] The computer device 700 may be a terminal or a server. The computer device 700 includes a processor 720 , a memory, and a network interface 750 connected via a system bus 710 , wherein the memory may include a non-volatile storage medium 730 and an internal memory 740 .

[0101] The non-volatile storage medium 730 may store an operating system 731 and a computer program 732. When the computer program 732 is executed, the processor 720 may execute an automatic test method for image sensor production.

[0102] The processor 720 is used to provide computing and control capabilities and support the operation of the entire computer device 700.

[0103] The internal memory 740 provides an environment for the operation of the computer program 732 in the non-volatile storage medium 730 . When the computer program 732 is executed by the processor 720 , the processor 720 can execute the automatic test method for the production of image sensors.

[0104] The network interface 750 is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will appreciate that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the present application scheme, and does not constitute a limitation on the computer device 700 to which the present application scheme is applied. The specific computer device 700 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. The processor 720 is used to run the program code stored in the memory to implement the automatic test method for image sensor production.

[0105] Those skilled in the art will understand that Figure 3 The embodiments of the computer device shown in the figure do not constitute a limitation on the specific composition of the computer device. In other embodiments, the computer device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, in some embodiments, the computer device may only include a memory and a processor. In such embodiments, the structure and function of the memory and the processor are the same as those of the embodiment of the present invention. Figure 3 The embodiments shown are consistent and will not be described again here.

[0106] It should be understood that in the embodiment of the present application, the processor 720 may be a central processing unit (CPU), and the processor 720 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0107] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium may be a non-volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the image sensor production automatic testing method disclosed in the embodiment of the present invention is implemented.

[0108] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the above-described equipment, devices and units can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. Those of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0109] In the several embodiments provided by the present invention, it should be understood that the disclosed equipment, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. Units with the same function may also be combined into one unit. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices or units, or may be electrical, mechanical or other forms of connection.

[0110] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0111] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0112] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), disk or optical disk and other media that can store program codes.

[0113] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An automatic test method for image sensor production, characterized in that: include: Acquire the image captured by the image sensor; Determine the bad pixels in the image; Determine the bad pixel cluster of the image according to the bad pixels in the image; Whether the image output of the image sensor is normal is determined based on the bad pixels and bad pixel clusters.

2. The automatic testing method for image sensor production according to claim 1, characterized in that: The step of acquiring an image captured by an image sensor comprises: Acquire an image captured by an image sensor in a black field environment; Get the image picture collected by the image sensor in a white field environment.

3. The automatic testing method for image sensor production according to claim 1, characterized in that: The bad pixels include dark pixels and bright pixels, and the step of determining the bad pixels in the image includes: Calculate the pixel value of the target pixel; Calculate the average pixel value of the surrounding pixels of the target pixel; If the pixel value of the target pixel is less than the average pixel value of the surrounding pixels, the target pixel is determined to be a dark point; If the pixel value of the target pixel is greater than the average pixel value of the surrounding pixels, the target pixel is determined to be a bright spot.

4. The automatic testing method for image sensor production according to claim 1, characterized in that: The step of determining the bad pixel clusters of the image according to the bad pixels in the image includes: Calculate whether the number of consecutive bad pixels in the image is greater than or equal to a set bad pixel threshold; If so, the pixel area formed by the continuous bad pixels is determined as a bad pixel cluster.

5. The automatic testing method for image sensor production according to claim 1, characterized in that: The image screen is divided into a first area and a second area, and judging whether the image output screen of the image sensor is normal according to the bad pixels and the bad pixel clusters includes: Calculating the number of bad pixels and the number of bad pixel clusters in the first area and the second area respectively; If the number of bad pixels and the number of bad pixel clusters in the first area are both less than the set first standard threshold, and the number of bad pixels and the number of bad pixel clusters in the second area are both less than the set second standard threshold, it is determined that the photosensitivity imaging of the image sensor is normal.

6. The automatic testing method for image sensor production according to claim 5, characterized in that: The first area is a central area of ​​the image screen, and the second area is a peripheral area of ​​the image screen, and the peripheral area is located outside the central area.

7. The automatic testing method for image sensor production according to claim 6, characterized in that: The area of ​​the peripheral region is greater than the area of ​​the central region.

8. Image sensor production automatic test device, characterized in that: include: An acquisition unit, used to acquire an image captured by an image sensor; A bad pixel determination unit, used to determine bad pixels in the image; A bad pixel cluster determination unit, used to determine the bad pixel clusters of the image according to the bad pixels in the image; The judging unit is used to judge whether the image output picture of the image sensor is normal according to the bad pixels and the bad pixel clusters.

9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for automatic production testing of an image sensor as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the automatic production test method of the image sensor according to any one of claims 1 to 7.

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