Image acquisition method and device, electronic equipment, storage medium and program product

Abstract

The invention provides an image acquisition method and apparatus, an electronic device, a storage medium and a program product. The method comprises the steps of obtaining an initial imaging parameter of an imaging unit for shooting an image of a target object and an evaluation parameter for evaluating the image of the target object; based on the initial imaging parameter, controlling an imaging unit to shoot a target object to obtain a first image; extracting a first detection area corresponding to the target object; calculating a first image parameter; evaluating the first image according to the evaluation parameter and the first image parameter to obtain an evaluation result of the first image; in response to the fact that the evaluation result does not meet the requirement, adjusting the initial imaging parameter based on the first image parameter to obtain the first imaging parameter; and based on the first imaging parameter, controlling the imaging unit to shoot the target object again to obtain a second image. According to the invention, automatic adjustment can be realized, so that the industrial detection efficiency is improved, and the accuracy and reliability of optical detection are improved.

Description

Technical Field

[0001] This disclosure relates to the field of computer vision technology, and in particular to an image acquisition method, apparatus, electronic device, storage medium, and program product. Background Technology

[0002] This section is intended to provide background or context for the embodiments of this disclosure as set forth in the claims. The description herein is not intended to be a prior art simply because it is included in this section.

[0003] In modern industrial production, automated optical inspection (AOI) technology has become a crucial step in ensuring product quality. As manufacturing demands for product quality continue to rise, the application of AOI technology is becoming increasingly widespread, encompassing multiple fields such as electronics manufacturing and the automotive industry. When using traditional grayscale cameras for product inspection, it is often necessary to manually adjust the exposure time and gain according to the different characteristics of the product to obtain an image that meets the requirements.

[0004] However, the relevant technologies have not yet achieved the function of automatically adjusting imaging parameters, and there are problems such as low efficiency and poor stability. Summary of the Invention

[0005] In view of this, the purpose of this disclosure is to provide an image acquisition method, device, electronic device, storage medium and program product, which at least to some extent solves one of the technical problems in the related art.

[0006] To achieve the above objectives, the first aspect of this exemplary embodiment provides an image acquisition method, comprising: Acquire initial imaging parameters for the imaging unit used to capture an image of the target object, and evaluation parameters for evaluating the image of the target object; Based on the initial imaging parameters, the imaging unit is controlled to capture an image of the target object to obtain a first image; Extract the first detection region corresponding to the target object from the first image; Calculate the first image parameters of the first detection region; The first image is evaluated based on the evaluation parameters and the first image parameters to obtain the evaluation result of the first image; In response to the evaluation result being unsatisfactory, the initial imaging parameters are adjusted based on the first image parameters to obtain the first imaging parameters; Based on the first imaging parameters, the imaging unit is controlled to re-capture the target object to obtain a second image.

[0007] Based on the same inventive concept, a second aspect of the exemplary embodiments of this disclosure provides an image acquisition device, including: The configuration parameter determination module is configured to acquire initial imaging parameters of the imaging unit for capturing an image of the target object and evaluation parameters for evaluating the image of the target object; The first image determination module is configured to control the imaging unit to capture a first image of the target object based on the initial imaging parameters. The detection region determination module is configured to extract a first detection region corresponding to the target object from the first image; The image parameter determination module is configured to calculate the first image parameters of the first detection region; The evaluation result determination module is configured to evaluate the first image based on the evaluation parameters and the first image parameters to obtain the evaluation result of the first image; An imaging parameter determination module is configured to adjust the initial imaging parameters based on the first image parameters in response to the evaluation result being unsatisfactory, so as to obtain the first imaging parameters. The second image determination module is configured to control the imaging unit to re-capture the target object based on the first imaging parameters to obtain a second image.

[0008] Based on the same inventive concept, a third aspect of the exemplary embodiments of this disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in the first aspect.

[0009] Based on the same inventive concept, a fourth aspect of the exemplary embodiments of this disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described in the first aspect.

[0010] Based on the same inventive concept, a fifth aspect of the exemplary embodiments of this disclosure provides a computer program product including computer program instructions that, when run on a computer, cause the computer to perform the method as described in the first aspect.

[0011] As can be seen from the above description, the image acquisition method, apparatus, electronic device, storage medium, and program product provided in this disclosure include: acquiring initial imaging parameters of an imaging unit for capturing an image of a target object and evaluation parameters for evaluating the image of the target object; controlling the imaging unit to capture an image of the target object based on the initial imaging parameters to obtain a first image; extracting a first detection region corresponding to the target object from the first image; calculating first image parameters of the first detection region; evaluating the first image according to the evaluation parameters and the first image parameters to obtain an evaluation result of the first image; adjusting the initial imaging parameters based on the first image parameters in response to the evaluation result being unsatisfactory to obtain first imaging parameters; and controlling the imaging unit to re-capture the target object based on the first imaging parameters to obtain a second image. This disclosure enables automated adjustment, thereby improving industrial inspection efficiency and enhancing the accuracy and reliability of optical inspection. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in this disclosure or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A schematic diagram of a background art adjustment flowchart for an image acquisition method provided as an exemplary embodiment of this disclosure; Figure 2 A schematic diagram of an adjustment process for an image acquisition method provided as an exemplary embodiment of this disclosure; Figure 3 A schematic diagram illustrating an application scenario of the image acquisition method provided in this exemplary embodiment of the disclosure; Figure 4 A schematic flowchart of an image acquisition method provided as an exemplary embodiment of this disclosure; Figure 5 A schematic diagram of a system architecture for an image acquisition method provided as an exemplary embodiment of this disclosure; Figure 6 A schematic diagram of the structure of an image acquisition device provided for an exemplary embodiment of this disclosure; Figure 7 A schematic diagram of the hardware structure of an electronic device provided for an exemplary embodiment of this disclosure. Detailed Implementation

[0014] It is understood that before using the technical solutions disclosed in the various embodiments of this application, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this application in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0015] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this application's technical solution, based on the prompt message.

[0016] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose whether to "agree" or "disagree" to provide personal information to the electronic device.

[0017] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this application. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this application.

[0018] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0019] To make the objectives, technical solutions, and advantages of this disclosure clearer, the principles and spirit of this disclosure will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement this disclosure, and are not intended to limit the scope of this disclosure in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.

[0020] In this article, it is important to understand that any number of elements in the accompanying figures is for illustrative purposes and not for limitation, and any naming is for distinction only and has no limiting meaning.

[0021] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this disclosure should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar words used in the embodiments of this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly. The article "a" or "an" preceding an element does not exclude the existence of multiple such elements.

[0022] The principles and spirit of this disclosure will be explained in detail below with reference to several representative embodiments.

[0023] As described in the background section, automated adjustment functionality has not yet been achieved in related technologies, and problems such as low efficiency and poor stability exist. Specifically, AOI (Automated Optical Inspection) systems play a crucial role in various process stages of display panel manufacturing. (Reference) Figure 1 For example, the operator uses a control system to provide grayscale voltage (e.g., G64 image (an image where all pixels have a grayscale value of 64)), red (R) image voltage, blue (B) image voltage, and / or other image voltages to the light source (PG light source) of the lighting equipment to illuminate the PG light source. This allows the display panel to display under the backlight of the PG light source corresponding to the G64 image, R image, B image, and / or other images. The display panel displaying these images is then photographed using an industrial camera. After capturing the images, the operator manually checks the grayscale of each image. If the grayscale does not meet the requirements, the camera's exposure time and gain are manually adjusted, and the image is reshot until the image quality meets the requirements. The image parameters at the point where the image quality meets the requirements are then saved, ensuring that the AOI optical inspection system can acquire high-quality images of the displayed images for subsequent inspection. Subsequently, visual algorithms are used to analyze the images to identify potential process defects, such as bright spots, dark spots, light leaks, and color differences. The system further grades the detected defects to assess their severity, thereby ensuring that problems in the production process can be identified and corrected in a timely manner. Finally, the grading results are uploaded in real time to the central control system or database via the DFS (Data Transfer System) for subsequent quality analysis and tracking.

[0024] However, the inventors of this disclosure have discovered that different products possess different characteristics during the manufacturing process of display panels, thus requiring the capture of different inspection images to meet the stringent grayscale brightness requirements of AOI optical inspection systems. Due to the differences in optical characteristics, surface reflectivity, and inspection needs of each product, the camera's exposure time, gain value, and other imaging parameters all require precise adjustment. This adjustment process typically requires manual operation by experienced technicians based on specific circumstances, and due to the complexity and diversity of products, adjusting camera imaging parameters to achieve the ideal grayscale brightness value often takes 3 to 6 hours. This lengthy adjustment not only reduces production efficiency and increases labor costs but also easily leads to unstable image quality due to differences in operator experience, thereby affecting subsequent inspection accuracy and product quality control.

[0025] Furthermore, the lack of automated adjustment functions in related technologies stems primarily from the reliance on operator experience and manual adjustments in traditional industrial inspection processes. In real-world industrial environments, different products exhibit varying characteristics, and changes in lighting conditions and the environment can affect imaging results. Therefore, operators must manually adjust exposure time and gain based on these factors before each inspection. This process not only increases labor costs but can also lead to instability during adjustment, thereby impacting the reliability of the inspection results.

[0026] Furthermore, the limitations of traditional methods often result in imaging results that fail to meet the requirements of optical inspection. Manually adjusting exposure time and gain is cumbersome, time-consuming, and labor-intensive, typically requiring experienced operators. However, high operator turnover hinders the accumulation of technical expertise. Moreover, differing adjustment standards among operators lead to inconsistent image quality, impacting subsequent product defect detection results. This unstable imaging quality directly affects subsequent quality control processes, reducing inspection efficiency and image quality, making it difficult to meet the high efficiency and high precision requirements of modern industrial production.

[0027] To address, at least partially, the above-mentioned problems, this disclosure provides an image acquisition method, apparatus, electronic device, storage medium, and program product solution. The method includes: The method involves acquiring initial imaging parameters for an imaging unit used to capture an image of a target object, as well as evaluation parameters for evaluating the image of the target object. Based on the initial imaging parameters, the imaging unit is controlled to capture an image of the target object to obtain a first image. A first detection region corresponding to the target object is extracted from the first image. First image parameters of the first detection region are calculated. The first image is evaluated based on the evaluation parameters and the first image parameters to obtain an evaluation result for the first image. In response to the evaluation result being unsatisfactory, the initial imaging parameters are adjusted based on the first image parameters to obtain first imaging parameters. Based on the first imaging parameters, the imaging unit is controlled to re-capture the target object to obtain a second image. This disclosure reduces reliance on operator experience and allows for rapid adjustments through strategies during the imaging adjustment process.

[0028] refer to Figure 2 The operator first initiates an automatic debugging algorithm (e.g., the image acquisition method of this disclosure embodiment or at least a portion of that method). Then, the PG light source is activated to provide backlighting for the display panel, thereby sequentially displaying a G64 image, a black image, a B image, and other images. After the imaging unit (e.g., an industrial camera) captures these images, the system automatically extracts the detection area from the image (e.g., the display area of ​​the display panel or the illuminated area (cell area)) and calculates the image parameters (e.g., average grayscale value) of that area. Based on the image parameters, it is determined whether the image quality meets the requirements. If not, the algorithm automatically iteratively adjusts the camera's exposure time and gain until the image parameters meet the preset standards. Finally, the iteration results are recorded and saved. Based on this, the time required for image acquisition can be significantly reduced, improving industrial inspection efficiency; and ensuring that the image parameters of the acquired images remain stable within a preset range, improving the accuracy and reliability of optical inspection.

[0029] After introducing the basic principles of this disclosure, various non-limiting embodiments of this disclosure will be described in detail below.

[0030] refer to Figure 3 This is a schematic diagram illustrating an application scenario of the image acquisition method provided by an exemplary embodiment of this disclosure.

[0031] In this application scenario, the image acquisition system may include a target object 101, an imaging unit 102, a terminal device 103, and a server 104. The imaging unit 102, the terminal device 103, and the server 104 can all be connected via wired or wireless communication networks to achieve data interaction.

[0032] The imaging unit 102 can be a device or apparatus capable of acquiring images of the target object 101, and can be controlled by instructions issued by the terminal device 103. Optionally, the imaging unit 102 can be an industrial camera, such as a camera tube, a charge-coupled device (CCD), a photoacoustic microscope, etc.

[0033] Terminal device 103 may be an electronic device located close to the user side, possessing data transmission and multimedia input / output functions, including but not limited to desktop computers, mobile phones, portable computers, tablet computers, media players, smart wearable devices, personal digital assistants (PDAs), or other electronic devices capable of performing the aforementioned functions. This electronic device may include a processor and a display screen with touch input functionality. The display screen is used to present a graphical user interface (GUI), which can display an application interface. The processor is used to process application data, generate the GUI, and control the display of the GUI on the screen.

[0034] Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0035] In some exemplary scenarios, a user (e.g., an operator) can control the imaging unit 102 to capture images of the target object 101 via the terminal device 103, and can acquire the images captured by the imaging unit 102 for image processing. Images with acceptable imaging quality can be uploaded to the server 104 for subsequent defect detection.

[0036] For example, the terminal device 103 can acquire the initial imaging parameters of the imaging unit 102 for capturing an image of the target object 101 and the evaluation parameters for evaluating the image of the target object 101.

[0037] The terminal device 103 can control the imaging unit 102 to capture the target object 101 to obtain a first image based on the initial imaging parameters.

[0038] The terminal device 103 can extract the first detection area corresponding to the target object 101 from the first image.

[0039] The terminal device 103 can calculate the first image parameters of the first detection area.

[0040] Terminal device 103 can evaluate the first image based on the evaluation parameters and the first image parameters to obtain the evaluation result of the first image.

[0041] In response to the evaluation result being unsatisfactory, the terminal device 103 may adjust the initial imaging parameters based on the first image parameters to obtain the first imaging parameters.

[0042] After the terminal device 103 controls the imaging unit 102 to re-capture the target object 101 based on the first imaging parameters to obtain a second image, the terminal device 103 can transmit the second image to the server 104.

[0043] It should be noted that the above application scenarios are shown only to facilitate understanding of the spirit and principles of this disclosure, and the implementation of this disclosure is not limited in any way. It is understood that the implementation of this disclosure can be applied to any applicable scenario.

[0044] In some embodiments, reference Figure 4 The image acquisition method provided in this disclosure may further include the following steps.

[0045] In step S410, the initial imaging parameters of the imaging unit used to capture an image of the target object and the evaluation parameters for evaluating the image of the target object can be obtained.

[0046] Optionally, the target object can refer to a product or component that needs to be inspected during the industrial image acquisition process, such as a display panel, chip, circuit board, or mechanical part. In a more specific scenario, the image acquisition method of this disclosure embodiment can be used to acquire images of a display panel, and the target object can be the display panel, such as a liquid crystal display panel.

[0047] Alternatively, the imaging unit may refer to a device used to capture images of a target object; for example, an industrial camera, a digital camera, or a video camera.

[0048] In some embodiments, initial imaging parameters may refer to a series of initial parameters set by the imaging unit (such as an industrial camera) before image capture of a target object (such as a display panel) begins. These parameters determine the basic imaging characteristics during image capture and have a significant impact on the quality of the final image.

[0049] In some embodiments, evaluation parameters may refer to a set of standards or quantitative indicators used to analyze, evaluate or quality control images or parameters. These parameters are used to measure specific properties of images or data to determine whether they meet predetermined requirements or standards.

[0050] In some embodiments, during image acquisition, the image acquisition system can record anomalies generated in the process through anomaly log management, thereby enabling the backtracking of abnormal situations in the process based on the anomaly log information. As a specific embodiment, refer to... Figure 5 The image acquisition system starts and acquires initial imaging and evaluation parameters. It then provides a light source signal via a PG light source. If the light source is abnormal, the anomaly is recorded in the anomaly log, allowing technicians to handle the issue. Next, if the light source is normal, the system sends a capture signal to obtain an image. If the capture is abnormal, this is also recorded in the anomaly log, enabling technicians to handle the issue. Afterward, if the capture is normal, the system identifies and extracts cell regions from the image and calculates image parameters. If there is a variance discrepancy between the image parameters and the evaluation parameters, this is recorded in the anomaly log, allowing technicians to handle the issue. If the adjustment is successful, the system iteratively adjusts and saves the parameters to optimize the image acquisition effect.

[0051] Taking the acquisition of display panel image information under a white background (e.g., all pixels of the display panel are displayed according to a grayscale value of 255) by an industrial camera through an image acquisition system as an example: The image acquisition system generates evaluation parameters based on the parameters set by relevant technicians to evaluate the white background image information of the display panel, and obtains the initial imaging parameters of the industrial camera. Technicians can pre-set a series of evaluation parameters according to inspection needs, such as the grayscale value, contrast, and color accuracy of the white background. These evaluation parameters can be set according to the evaluation standards of the white background image parameters of the display panel; alternatively, technicians can conduct numerous experiments to determine that the evaluation parameters should be within a specific threshold range. Such settings can more effectively improve the inspection results of the display panel. These evaluation parameters are input into the image acquisition system to guide its software configuration and algorithm development; the system then uses these evaluation parameters to automatically evaluate the quality of the captured image through its built-in image analysis algorithm to ensure that it meets the predetermined standards. This process includes testing and optimizing the algorithm to ensure the accuracy of the evaluation, ultimately generating a complete set of evaluation parameters for image acquisition and quality control, achieving efficient and accurate analysis of images of target objects such as display panels.

[0052] In step S420, the imaging unit can be controlled to capture a first image of the target object based on the initial imaging parameters.

[0053] In this step, the imaging unit can be controlled to acquire the first image of the target object based on the initial imaging parameters, which will be used for further processing in subsequent steps.

[0054] In some embodiments, the initial imaging parameters may include: initial exposure time and initial gain; based on the initial imaging parameters, controlling the imaging unit to capture an image of the target object to obtain a first image may further include: Based on the initial exposure time and the initial gain, the imaging unit is controlled to capture an image of the target object to obtain the first image.

[0055] Optionally, the initial exposure time can refer to the length of time the sensor of an imaging unit (such as a camera) is exposed to light to capture an image. Exposure time determines the amount of light received by the sensor, thus affecting the brightness of the image. If the exposure time is too long, the image may be too bright, resulting in loss of detail; if the exposure time is too short, the image may be too dark, also failing to capture sufficient detail. The initial exposure time is a starting value set before shooting and can be adjusted according to ambient lighting conditions and the reflective properties of the target object.

[0056] Optionally, gain can refer to the degree of signal amplification in the image sensor. Initial gain can be a starting signal amplification value set before image acquisition begins, used to adjust image brightness. In low-light conditions, increasing gain can help improve image brightness, making details more visible. However, excessively high gain may increase image noise, affecting image quality. The initial gain also needs to be set appropriately based on the actual shooting conditions and the characteristics of the target object.

[0057] Following the exemplary embodiments described above, refer to Figure 5 In some embodiments, taking the acquisition of display panel image information under a white screen by an industrial camera as an example, the image acquisition system first obtains the initial imaging parameters of the industrial camera used to acquire the display panel image under a white screen. The initial imaging parameters include: initial exposure time and initial gain, which are basic settings of the industrial camera before shooting. The image acquisition system provides a light source signal through a PG light source; under the control of the light source signal, the imaging unit takes a picture of the target object according to the set initial exposure time and initial gain to obtain a first image.

[0058] In step S430, the first detection region corresponding to the target object can be extracted from the first image.

[0059] refer to Figure 2The imaging unit acquires images of the illuminated display panel. Therefore, in addition to the display area, the non-display area and peripheral areas of the display panel may also be captured in the image. However, subsequent defect detection typically targets the display area. Therefore, in this step, the area corresponding to the display area of ​​the display panel is extracted from the first image as the first detection area. This ensures that subsequent processing based on this detection area and the final adjustment result make the image acquired by the imaging unit beneficial for subsequent defect detection.

[0060] In some embodiments, extracting the first detection region corresponding to the target object from the first image may further include the following steps: The first image is denoised to obtain a denoised first image; The first image after denoising is binarized to obtain the first detection region.

[0061] Following the exemplary embodiments described above, refer to Figure 5 In some embodiments, taking the acquisition of a display panel image under a white background by an industrial camera as an example, the industrial camera captures the display panel according to a preset initial exposure time and initial gain. After the capture is completed, the first image data (in the form of a digital signal) is temporarily stored in the camera memory. The image acquisition system reads the first image data from the camera memory through a data transmission interface (such as USB, GigE, etc.). The read data is usually represented in 8-bit bytes (i.e., 8 bits per color channel), which means that the color information of each pixel consists of 3 bytes (1 byte each for red, green, and blue), for a total of 24 bits. The read image is stored in a matrix variable matOrigin in the image acquisition system. This matrix is ​​a two-dimensional array, where each element represents the color value of a pixel. matOrigin contains the raw data of the first image, without any processing or adjustment, and can therefore be regarded as the "raw form" of the first image. The "original form" of the first image is denoised. Gaussian filtering can be applied to smooth the "original form" of the first image. Gaussian filtering is a linear filtering technique that uses a Gaussian function as a weighting function to calculate the weighted average of neighboring pixels, thereby reducing image noise and preserving edge information. Gaussian filtering helps remove high-frequency noise from the image while maintaining the image's main features, such as edges and textures, thus obtaining the denoised first image.

[0062] In some embodiments, a binarization operator can also be applied to transform the denoised first image; binarization is a process of converting image pixel values ​​into values ​​with only two possible values ​​(usually 0 and 1 or black and white). Binarization helps to highlight target regions in the image (such as cell regions; i.e., the detection regions in this scheme) and simplifies subsequent image analysis.

[0063] In step S440, the first image parameters of the first detection region can be calculated.

[0064] Optionally, the first image parameter can be any type of image parameter corresponding to the first detection region, such as grayscale value, contrast, chromaticity value, etc. It is understood that any image parameter that can be used to evaluate the image capture quality can be considered as an image parameter in the embodiments of this disclosure.

[0065] In some embodiments, the average grayscale value can be used as the first image parameter, which can better reflect the overall shooting quality of the image and make the calculation easier.

[0066] Optionally, if the extracted first detection region is not a grayscale image, it can be converted to a grayscale image first. For color images, the conversion to grayscale can be achieved using a weighted average method or other methods, converting the values ​​of the RGB three channels into a single pixel value; the weighted average conversion formula can be expressed as: ; in, and These are the values ​​for the red (R), green (G), and blue (B) channels, respectively.

[0067] Optionally, the pixel value corresponding to each pixel in the first detection region can be calculated as the grayscale value according to the above calculation formula. Alternatively, if necessary, it can be further converted into a standard 8-bit grayscale image, where the grayscale value ranges from 0 to 255.

[0068] After calculating the grayscale value of each pixel, the average grayscale value can be further calculated as the first image parameter.

[0069] Optionally, the average grayscale value can be obtained by summing the grayscale values ​​of all pixels corresponding to the first detection area and then dividing by the number of all pixels corresponding to the first detection area.

[0070] In step S450, the first image is evaluated according to the evaluation parameters and the first image parameters to obtain the evaluation result of the first image.

[0071] In this step, after calculating the first image parameters of the first detection region, the quality of the first image can be evaluated based on the first image parameters and the aforementioned evaluation parameters.

[0072] In some embodiments, the evaluation parameters may include: a variance threshold and target image parameters; The step of evaluating the first image based on the evaluation parameters and the first image parameters to obtain the evaluation result of the first image may further include the following steps: Determine the image variance of the first image, and evaluate the image variance based on the variance threshold; In response to the image variance being less than the variance threshold, the lens parameters of the imaging unit are adjusted; In response to the image variance being greater than or equal to the variance threshold, the first image parameters are evaluated based on the target image parameters to obtain the evaluation result.

[0073] Optionally, the variance threshold can be a preset threshold used to determine the sharpness of the first image, specifically whether the grayscale value distribution of the image is uniform. If the image variance is less than this threshold, it may indicate that the image is blurry and cannot clearly present detailed information, requiring a re-shoot.

[0074] Optionally, the target image parameters may refer to a series of standards or quantitative indicators used to evaluate and optimize image quality during the image acquisition and processing process in this scheme.

[0075] In some embodiments, image variance can be calculated in the following ways: Obtain the grayscale value of each pixel in the first detection region, and calculate the average grayscale value of the first detection region based on the grayscale value of each pixel. The average grayscale value ( The average grayscale value of all pixels in the image is calculated using the following formula: ; in: This represents the total number of pixels in the image; It is the first The grayscale value of each pixel.

[0076] Sum the squares of the differences between the grayscale values ​​of all pixels and the average grayscale value, then divide by the total number of pixels. The average of the squared differences is obtained, which is the image variance. : ; In this way, the image variance can be calculated and compared with the variance threshold, thereby roughly determining whether the image quality is affected by the shooting quality.

[0077] Use variance threshold To indicate, for reference Figure 5 In some embodiments, if If the image quality is poor, it indicates a quality issue with the first image. A message will appear stating "Current image is abnormal; please adjust the focal length and aperture of the imaging unit," and this will be recorded in the error log. Since the first image capture is faulty, a retake is necessary. Therefore, the operator can readjust the lens parameters of the imaging unit (e.g., focal length, aperture, etc.). Focal length changes the magnification and angle of view, affecting image detail and contrast. The focal length can be adjusted as needed to bring the lens closer to or further away from the target object. Aperture affects the amount of light and depth of field. Adjusting the aperture value to decrease it increases the depth of field, making the image sharper, but may reduce image brightness.

[0078] On the other hand, if the image variance is greater than or equal to the variance threshold, it indicates that the image sharpness is appropriate, suggesting that the lens parameters should be fine. Therefore, the image quality of the first image can be further evaluated from other aspects.

[0079] In some embodiments, the first image parameter may include the average grayscale value of the image, and the target image parameter may include a grayscale value threshold range; The evaluation of the first image parameters based on the target image parameters to obtain the evaluation result may further include the following steps: Determine whether the average grayscale value is within the grayscale value threshold range; In response to determining that the average grayscale value is within the grayscale value threshold range, the evaluation result is determined to meet the requirements; In response to determining that the average grayscale value is not within the grayscale value threshold range, the evaluation result is determined to be non-compliant.

[0080] Optionally, the average grayscale value can refer to the average of the grayscale values ​​of all pixels in the image, i.e., the average grayscale value in the exemplary embodiment above. The grayscale value can range from 0 (black) to 255 (white). In this exemplary embodiment, it refers to the average grayscale value of the first image. .

[0081] Optionally, the grayscale threshold range can refer to a set of standards used to define the ideal range in which the average grayscale value of an image should fall. These threshold ranges are crucial for image processing, analysis, and quality control because they ensure that images meet specific visual and analytical requirements. A suitable grayscale threshold range can be determined by analyzing a large number of sample images; no specific limitations are specified here.

[0082] In the above exemplary embodiment, the average grayscale value of the first image can be obtained. In this step, the first average grayscale value of the first image can be calculated. The image quality of the first image is compared with the grayscale threshold range to determine whether the image quality meets the requirements.

[0083] Optionally, the grayscale threshold range can be a target grayscale threshold. The first average grayscale value of the first image obtained The target grayscale value threshold in the set target image parameters If a comparison is made, If so, it can be assumed that the image quality of the first image meets the requirements, indicating that the quality of the first image of the current scene is not a problem, and the imaging parameters of the imaging unit do not need to be adjusted.

[0084] As an optional embodiment, refer to Figure 5 Taking the image acquisition system acquiring display panel image information under a white background using an industrial camera as an example, the target grayscale threshold set by the image acquisition system for the display panel image information under a white background is... This is the expected average grayscale value, representing the ideal image brightness level. Simultaneously, the system defines the range of average grayscale values ​​that meet the test requirements. ,in This represents the minimum value within the grayscale threshold range, in this exemplary embodiment. ; This represents the maximum value within the grayscale threshold range, in this exemplary embodiment. It can be seen that this threshold range can be based on the target grayscale value threshold. To set a threshold value, such that the average grayscale value of the image is within that threshold range, it can be considered that... Therefore, the image quality can be considered to meet the requirements. For example, the average grayscale value of the display panel image under the current white screen is measured by an image acquisition system. With respect to the preset grayscale threshold range Comparison, because It can be determined The image is within the grayscale threshold range. Therefore, the current image quality can be considered to meet the test requirements, and there is no need to adjust the parameters of the current industrial camera.

[0085] On the other hand, if the average grayscale value is not within the grayscale value threshold range, it can be determined that the evaluation result does not meet the requirements, and the imaging parameters of the imaging unit need to be further adjusted.

[0086] Following the exemplary embodiments described above, refer to Figure 5 Taking the image acquisition system using an industrial camera to capture image information of a display panel under a white background as an example, the grayscale threshold set by the image acquisition system for the display panel image information under a white background... Meanwhile, the system defines the average grayscale value range that meets the test requirements. ,in This represents the minimum value within the grayscale threshold range, in this exemplary embodiment. ; This represents the maximum value within the grayscale threshold range, in this exemplary embodiment. The average grayscale value of the display panel image under the current white screen is measured using an image acquisition system. With respect to the preset grayscale threshold range By comparison, since 53 < 55, it can be determined that... The image is not within the grayscale threshold range. Therefore, it can be concluded that the current image quality does not meet the test requirements, and the current industrial camera parameters need to be adjusted.

[0087] In step S460, in response to the evaluation result being unsatisfactory, the initial imaging parameters can be adjusted based on the first image parameters to obtain the first imaging parameters.

[0088] In this step, since the evaluation result is unsatisfactory, and this unsatisfactory result may be due to the initial imaging parameters not meeting the requirements, the initial imaging parameters can be adjusted. Furthermore, since the first image parameter reflects image quality, this step can adjust the initial imaging parameters based on the first image parameter, thereby potentially improving the image quality captured by the imaging unit using the adjusted first imaging parameters.

[0089] In some embodiments, the evaluation parameters further include: a target scaling factor and a scaling factor threshold range; Adjusting the initial imaging parameters based on the first image parameters to obtain the first imaging parameters includes: Based on the target image parameters, the first image parameters, and the target scaling factor, a first adjustment scaling factor is determined; Determine whether the first adjustment ratio coefficient is within the range of the ratio coefficient threshold; In response to the first adjustment ratio coefficient not being within the range of the ratio coefficient threshold, the initial imaging parameters are adjusted according to the first adjustment ratio coefficient to obtain the first imaging parameters.

[0090] Optionally, the target scaling factor can be a coefficient used to measure the proportional relationship between the current image grayscale value and the target grayscale value. In the algorithm, it plays a role in adjusting the exposure time and gain to ensure that the image's grayscale values ​​can quickly and accurately reach the preset target range.

[0091] Optionally, the scaling factor threshold range can refer to a range of values ​​based on the target scaling factor, which can be used to determine whether the image grayscale value is within the preset target range.

[0092] Optionally, the first adjustment ratio coefficient may refer to an adjustment ratio coefficient corresponding to the exposure time and gain of the image for dynamically adjusting the image, and is used to determine whether the grayscale value of the image reaches the preset target range.

[0093] Optionally, the first imaging parameters may refer to a series of imaging settings used in the first attempt to capture an image of the target object. These parameters ensure that the camera or other imaging unit can capture an image that meets specific quality requirements, typically such as exposure time and gain.

[0094] In some embodiments, the first adjustment ratio coefficient The parameter can be calculated using the target image parameters, the first image parameters, and the target scaling factor, and is used to measure the first average grayscale value in the first image parameters. and target grayscale value The proportional relationship between them can be expressed by the formula: ; in, It can be the target ratio coefficient.

[0095] In some embodiments, it can be based on a target scaling factor. A scaling factor threshold range is set, which can be a preset numerical interval, to determine whether the grayscale values ​​of the current image meet the target requirements. Optionally, the scaling factor threshold range can be based on the target scaling factor. Set a reasonable fluctuation range around the center. The threshold range of the proportional coefficient can be expressed as: ; in, This is the target proportionality coefficient, which, for example, can be set to 1; The threshold range for the scaling factor is usually a small positive number, such as 0.05.

[0096] like, Within the threshold range of the proportional coefficient, i.e. If the exposure time of the first image is less than the maximum exposure time set, then the average grayscale value of the first image is considered to meet the target requirements and no further adjustment is needed.

[0097] like, Not within the threshold range of the scaling factor, i.e. or If so, the initial imaging parameters need to be adjusted.

[0098] In some embodiments, adjusting the initial imaging parameters to obtain the first imaging parameters may further include the following steps: The initial imaging parameters are adjusted according to the first adjustment ratio coefficient to obtain the first imaging parameters.

[0099] In some embodiments, the first exposure time in the first imaging parameter It can be based on the initial exposure time and the first adjustment ratio coefficient The calculation yielded the following results: ; This formula means that the exposure time needs to be based on... The value is linearly adjusted to obtain the first imaging parameter, which is then used to match the brightness of the displayed image.

[0100] In the above exemplary embodiments, one method for obtaining the first imaging parameter was described. Hereinafter, another method for obtaining the first imaging parameter is described.

[0101] In some embodiments, the target object may be a display panel illuminated by a lighting device (e.g., a display panel displaying an image under backlight provided by a PG light source). The image acquisition method of this disclosure embodiment may further include obtaining adjustment parameters of the imaging parameters of the imaging unit, the adjustment parameters may include: exposure time offset and refresh rate coefficient of the lighting device.

[0102] Furthermore, in this exemplary embodiment, the method for obtaining the first imaging parameter further includes: The initial exposure time is adjusted according to the first adjustment ratio coefficient, the refresh rate coefficient, and the exposure time offset to obtain the first exposure time; The first imaging parameters are determined based on the first exposure time.

[0103] Optionally, the display panel illuminated by the lighting device can refer to a display panel that displays images under the backlight provided by the PG light source of the lighting device. The PG light source is a specially designed light source that can provide illumination with specific patterns or structures. This illumination makes the image on the captured display panel clearly visible under specific conditions, helping to highlight certain defects of the display panel.

[0104] In some embodiments, reference Figure 5 In image acquisition systems, the adjustment parameters of imaging parameters are key factors in achieving precise adjustment of image grayscale values. These adjustment parameters can include exposure time offset and the refresh rate coefficient of the lighting equipment.

[0105] Among them, exposure time offset This can be a parameter used to adjust the exposure time, used for iterative parameter adjustments, and it plays a fine-tuning role when adjusting the exposure time. Its main purpose is to ensure that the adjustment of the exposure time can more accurately achieve the target grayscale value. In a real industrial environment, a suitable exposure time offset is determined through multiple experiments. Typically, this value needs to be adjusted based on the specific equipment and environmental conditions. For example, multiple images can be taken at different exposure times, the average grayscale value of each image can be calculated, and then a suitable exposure time offset can be determined by comparing it with the target grayscale value. .

[0106] Refresh rate coefficient This can be calculated based on the refresh rate of the lighting equipment to ensure that the exposure time is an integer multiple of the refresh rate. This avoids image quality issues, such as horizontal stripes, caused by a mismatch between the exposure time and the refresh rate. Optionally, the refresh rate coefficient... The calculation formula can be expressed as: ; in, This refers to the refresh rate of the lighting equipment, measured in Hertz (Hz). The refresh rate is an inherent parameter of the equipment and can usually be found in its technical manual or specifications.

[0107] For example, if the refresh rate of the lighting device is 122... Then refresh rate coefficient for: .

[0108] In some embodiments, adjusting the first exposure time The calculation formula can be: ; in, This indicates rounding up to the nearest integer.

[0109] Through the above steps, the first adjustment ratio can be dynamically adjusted. Refresh rate coefficient and exposure time offset Initial exposure time Fine-tuning is performed, but the adjustment must not deviate the exposure time from being an integer multiple of the refresh rate, thus obtaining the first exposure time. This process significantly improves the efficiency and stability of image acquisition.

[0110] In the above exemplary embodiments, two methods for obtaining the first imaging parameter were introduced. Below, we will introduce the adjustment method when the first exposure time is greater than the maximum exposure time in the evaluation parameters.

[0111] In this exemplary embodiment, in response to the first exposure time being greater than the maximum exposure time, the parameter value of the first exposure time is adjusted to the parameter value of the maximum exposure time.

[0112] Optionally, maximum exposure time This can be the maximum exposure time allowed by the camera or imaging system, usually measured in microseconds. The initial value of this parameter can be preset according to the camera's hardware characteristics, imaging environment, and application scenario to ensure that the image acquisition process is completed within a reasonable time range, while avoiding image blurring or other quality problems caused by excessive exposure time.

[0113] In some embodiments, in response to the first exposure time being greater than the maximum exposure time, adjusting the parameter value of the first exposure time to the parameter value of the maximum exposure time may be done by: if the adjusted first exposure time... Exceeded the maximum exposure time Then the adjusted exposure time can be set to This is to avoid image quality problems caused by excessive exposure time.

[0114] In step S470, based on the first imaging parameters, the imaging unit can be controlled to re-capture the target object to obtain a second image.

[0115] In this step, since the algorithm processing in the aforementioned embodiment yields new imaging parameters (first imaging parameters), the imaging unit can be controlled to re-capture the target object based on these new imaging parameters to obtain a second image. It can be understood that, due to the optimized imaging parameters, the image quality of the second image can be improved to a certain extent compared to the first image.

[0116] As a specific embodiment, let's take the image acquisition system using an industrial camera to capture image information of a display panel under a white background as an example, referring to... Figure 5 In the iterative parameter adjustment module of the image acquisition system, based on the first imaging parameters (including the adjusted exposure time)... (and initial gain), control the industrial camera to re-shoot the target object to obtain a second image.

[0117] In the above exemplary embodiments, a method for obtaining the second image is described, if, based on the first imaging parameters (including the adjusted exposure time) If the average grayscale value of the second image is within the grayscale value threshold range and the first adjustment ratio is within the ratio threshold range, it indicates that the current imaging parameters (exposure time and gain) can meet the image quality requirements.

[0118] Assuming an image acquisition system uses an industrial camera to capture an image of a display panel under a white background, and the refresh rate of the lighting product is 122 Hz, the average grayscale value of the current first image is... =30, target grayscale value threshold =50, grayscale value threshold range ,in This represents the minimum value within the grayscale threshold range, for example, ; This represents the maximum value within the grayscale threshold range, for example, Initial exposure time =500000μs, initial gain D=0.1, target scaling factor =1, threshold range of the scaling factor =0.05, exposure time offset =5000μs, adjust the scaling factor Refresh rate ,at this time This indicates that the current average grayscale value is too low, requiring an increase in exposure time. Therefore, the first exposure time can be further calculated. =840894 Use the first exposure time =840894 The initial gain value D=0.1 controls the imaging unit to re-capture the display panel to obtain a new image (e.g., a second image). Assume the average grayscale value of this new image is... And calculate again ,at this time exist If the image quality is within the specified range, it means that the current imaging parameters (exposure time and gain) are sufficient to meet the image quality requirements, and image acquisition for the next page can begin.

[0119] In some embodiments, if based on the first imaging parameters (including the adjusted exposure time) If the average grayscale value of the second image is not within the grayscale value threshold range, then further processing is performed using the following methods (and initial gain).

[0120] In this exemplary embodiment, a second detection region corresponding to the target object is extracted from the second image; Calculate the second image parameters of the second detection region; The second image is evaluated based on the evaluation parameters and the second image parameters to obtain the evaluation result of the second image; In response to the evaluation result being unsatisfactory, the first imaging parameters are adjusted based on the second image parameters to obtain the second imaging parameters; Based on the second imaging parameters, the imaging unit is controlled to re-capture the target object to obtain a third image.

[0121] Thus, if the evaluation result of the second image still does not meet the requirements, the imaging parameters can be further adjusted according to the aforementioned method, and the imaging unit can be controlled to re-capture the target object based on the new imaging parameters to obtain a third image. It is understood that, due to the further optimization of the imaging parameters, the image quality of the third image may be improved to some extent compared to the second image.

[0122] Optionally, the method for calculating the second image parameters of the second detection region is similar to the method for calculating the first image parameters of the first detection region, and will not be described again here.

[0123] In some embodiments, the evaluation result of the second image can be obtained by evaluating the second image based on the evaluation parameters and the second image parameters, or by taking the average grayscale value of the obtained second image. The target grayscale value threshold in the set target image parameters If a comparison is made, This means that the second image of the current screen does not need to be adjusted.

[0124] As a specific embodiment, refer to Figure 5 Taking the image acquisition system acquiring display panel image information under a white background using an industrial camera as an example, the target grayscale threshold set by the image acquisition system for the display panel image information under a white background is... This is the expected average grayscale value, representing the ideal image brightness level. Simultaneously, the system defines a grayscale value threshold range that meets the testing requirements. ,in This represents the minimum value within the grayscale threshold range, for example, ; This represents the maximum value within the grayscale threshold range, for example, The average grayscale value of the display panel image under the current white screen is measured using an image acquisition system. With respect to the preset grayscale threshold range Comparison, because It can be determined The image is within the grayscale threshold range. Therefore, the current image quality can be considered to meet the test requirements, and there is no need to adjust the current industrial camera parameters.

[0125] In other embodiments, it is assumed that the average grayscale value of the display panel image under the current white screen is measured by an image acquisition system. Compare it with the preset grayscale value threshold range By comparison, since 55 > 53, it can be determined that... The image is not within the grayscale threshold range. Therefore, it can be concluded that the current image quality does not meet the test requirements, and the current industrial camera parameters need to be adjusted.

[0126] In some embodiments, adjusting the first imaging parameters based on the second image parameters to obtain the second imaging parameters may be achieved by: fine-tuning and optimizing the first imaging parameters (exposure time and gain) based on the grayscale value analysis results of the second image to obtain second imaging parameters that more accurately meet the target grayscale value requirements.

[0127] In some cases, simply adjusting the exposure time may not be enough to achieve the desired effect, or the expected effect cannot be achieved by adjusting the exposure time alone. In such cases, other imaging parameters can be further adjusted.

[0128] Therefore, in some embodiments, the adjustment parameter may also include a gain offset; Adjusting the first imaging parameters based on the second image parameters to obtain the second imaging parameters includes: Based on the target image parameters, the second image parameters, and the target scaling factor, a second adjustment scaling factor is determined; Determine whether the second adjustment ratio is within the range of the ratio threshold; In response to the second adjustment ratio coefficient not being within the ratio coefficient threshold range, the first exposure time is adjusted according to the second adjustment ratio coefficient, the refresh rate coefficient, and the exposure time offset to obtain the second exposure time; In response to the first adjustment scaling factor being greater than the target scaling factor and the second adjustment scaling factor being less than the target scaling factor, or the first adjustment scaling factor being less than the target scaling factor and the second adjustment scaling factor being greater than the target scaling factor, the initial gain is adjusted according to the gain offset, the target scaling factor, the target image parameters and the second image parameters to obtain a first gain. The second imaging parameters are determined based on the second exposure time and the first gain.

[0129] Alternatively, gain offset can be another important adjustment parameter, used together with exposure time offset in the iterative parameter adjustment process to achieve precise control over imaging parameters such as gain. Typically, this value needs to be adjusted based on specific equipment and environmental conditions. For example, multiple images can be taken at different gain levels, the average grayscale value of each image can be calculated, and then a suitable gain offset can be determined by comparing it with the target grayscale value. .

[0130] In some embodiments, the second adjustment ratio coefficient The parameter is calculated using the target image parameters, the second image parameters, and the target scaling factor, and is used to measure the second average grayscale value in the second image parameters. and target grayscale value The proportional relationship between them can be expressed by the formula: .

[0131] Optionally, it can be based on the target scaling factor. A scaling factor threshold range is set, which can be a preset numerical interval, to determine whether the grayscale values ​​of the current image meet the target requirements. Optionally, the scaling factor threshold range can be based on the target scaling factor. Set a reasonable fluctuation range around the center. The threshold range of the proportional coefficient can be expressed as: ; in, This is the target proportionality coefficient, which, for example, can be set to 1; The threshold range for the scaling factor is usually a small positive number, such as 0.05.

[0132] like, Within the threshold range of the proportional coefficient, i.e. If the exposure time of the first image is less than the set maximum exposure time, then the grayscale value of the first image is considered to meet the target requirements and no further adjustment is needed.

[0133] like, Not within the threshold range of the scaling factor, i.e. or If so, the initial imaging parameters need to be adjusted.

[0134] Optionally, the second exposure time is calculated. The method can be: ; in, This indicates rounding up to the nearest integer.

[0135] Through the above steps, the second adjustment ratio can be dynamically adjusted. Refresh rate coefficient and exposure time offset For the first exposure time Adjustments were made to obtain the second exposure time. This allows for the determination of the second imaging parameters, which further improves the efficiency and stability of image acquisition.

[0136] refer to Figure 5 If based on the adjusted exposure time With the initial gain, the imaging unit camera is controlled to re-capture the target object so that the average grayscale value of the obtained second image is within the grayscale value threshold range, and the second adjustment ratio coefficient is within the ratio coefficient threshold range, indicating that the current imaging parameters (exposure time and gain) can meet the image quality requirements.

[0137] Optionally, in response to the first adjustment scaling factor being greater than the target scaling factor and the second adjustment scaling factor being less than the target scaling factor, or the first adjustment scaling factor being less than the target scaling factor and the second adjustment scaling factor being greater than the target scaling factor, the initial gain is adjusted according to the gain offset, the target scaling factor, the target image parameters, and the second image parameters to obtain the first gain. This can be achieved by: If the second average grayscale value obtained by controlling the imaging unit camera to re-shoot the target object through the adjusted exposure time and initial gain is still not within the target grayscale value range when the first adjustment ratio is greater than the target ratio, or if the second average grayscale value obtained by controlling the imaging unit camera to re-shoot the target object through the adjusted exposure time and initial gain is still not within the target grayscale value range when the first adjustment ratio is less than the target ratio, and the second adjustment ratio is greater than the target ratio, then it means that controlling the imaging unit to shoot the target object through the adjusted exposure time and initial gain is no longer able to make the average grayscale value of the target object's image close to the target grayscale value. Therefore, it is necessary to adjust the gain to make the average grayscale value of the target object's image close to the target grayscale value.

[0138] At this point, gain offset can be used. This is used to calculate the adjustment range of the gain. The gain offset is a preset parameter used to control the step size of each adjustment to avoid excessive adjustment that could lead to unstable image quality.

[0139] Based on the target proportion coefficient Initial gain Gain offset Target image parameters (target grayscale values) The second image parameter (if the average grayscale value of the first image is less than the target grayscale value, then the average grayscale value of the first image is selected). If the average grayscale value of the second image is less than the target grayscale value, then the average grayscale value of the second image shall be selected. The first gain was calculated. : .

[0140] As a specific embodiment, let's take the image acquisition system using an industrial camera to capture image information of a display panel under a white background as an example, referring to... Figure 5 In the iterative parameter adjustment module of the image acquisition system, based on the second imaging parameters (including the second exposure time)... and the first gain (This involves) controlling an industrial camera to re-photograph the target object to obtain a third image.

[0141] Furthermore, after obtaining the third image, if, based on the second imaging parameters (including the second exposure time) and the first gain If the imaging unit is controlled to re-capture the target object so that the average grayscale value of the third image is within the grayscale value threshold range and the second adjustment ratio coefficient is within the ratio coefficient threshold range, it indicates that the current imaging parameters (exposure time and gain) can meet the image quality requirements.

[0142] If, based on the second imaging parameters (including the second exposure time) and the first gain If the average grayscale value of the third image obtained by controlling the imaging unit to re-capture the target object is still not within the grayscale value threshold range, then the method further includes: In response to the number of times the imaging parameters of the imaging unit are adjusted reaching the maximum number of iterations, corresponding adjustment information is output, wherein the adjustment parameters also include the maximum number of iterations.

[0143] Optionally, the maximum number of iterations can refer to an upper limit on the number of iterations used to control the imaging parameter adjustment process. This parameter can prevent the system from looping infinitely during parameter adjustment, ensuring that the system completes the adjustment task within a reasonable time.

[0144] Optionally, refer to Figure 5 In the iterative parameter adjustment, if the number of iterations for adjusting the current image by the exposure time offset and exposure time offset exceeds the set maximum number of iterations, the iterative parameter adjustment module will prompt abnormal information (i.e., corresponding adjustment information) and record it in the abnormal log management. At this time, the image parameters of the imaging unit that acquires images of the target object need to be adjusted by the operator.

[0145] Assuming an image acquisition system uses an industrial camera to capture an image of a display panel under a white background, and the refresh rate of the lighting product is 122 Hz, the average grayscale value of the current image is... =30, target grayscale value threshold =50, grayscale value threshold range ,in This represents the minimum value within the grayscale threshold range, for example, ; This represents the maximum value within the grayscale threshold range, for example, Initial exposure time =500000μs, initial gain D=0.1, target scaling factor =1, threshold range of the scaling factor =0.05, exposure time offset =5000μs, gain offset =0.05, maximum number of iterations Adjust the scaling factor Refresh rate ,at this time This indicates that the current average grayscale value is too low, requiring an increase in exposure time. Further, the first exposure time is calculated. =840894μs, using the first exposure time The parameters 840894μs and initial gain D=0.1 control the imaging unit to re-capture the display panel to obtain a new image. Assume the average grayscale value of the image at this time... =55, then at this time 0.9 < Then, continue calculating the second exposure time according to the steps above. =761805μs, then based on the second exposure time An industrial camera is controlled to capture a new image using a gain of 761805 μs and an initial gain of D=0.1. Assume the average grayscale value of the new image at this point is... =54, at this time Then at this time Still less than the target ratio This indicates that adjusting the exposure time alone is insufficient to adjust the image; the initial gain D needs to be adjusted. In this case, the image acquisition system will select an image grayscale value smaller than the target grayscale. Calculate the first gain using a parameter of 30. ,use , A 761805μs control industrial camera is used to capture images of the display panel until the average grayscale value of the captured image is reached. .

[0146] Furthermore, if the display panel is photographed by adjusting the exposure time and gain parameters of the industrial camera, and the number of photographs exceeds 15, it indicates that the number of iterations has exceeded the maximum number of iterations. This means that despite multiple iterations, the camera's imaging parameters (exposure time, gain, etc.) have still failed to adjust the grayscale values ​​of the image to the target range, i.e., the image quality requirements have not been met. The automated parameter adjustment process may not be sufficient to solve the problem, and manual intervention by the operator is required to adjust the parameters based on experience or professional knowledge.

[0147] Based on the above exemplary embodiments, it can be illustrated that this application can achieve automated, efficient, and accurate acquisition of images of target objects (such as display panels). The method first obtains initial imaging parameters and evaluation parameters, then controls the imaging unit to capture a first image of the target object. Next, it extracts the detection region from the first image and calculates image parameters, then evaluates the image based on the evaluation parameters. If the image does not meet the requirements, the imaging parameters are adjusted based on the image parameters, and the capturing process is repeated until the image quality meets the requirements or the maximum number of iterations is reached. By dynamically adjusting parameters such as exposure time and gain, combined with image processing techniques such as denoising and binarization, this method can effectively improve the efficiency and stability of image acquisition, ensuring that the image quality meets predetermined standards, and is suitable for scenarios such as industrial inspection.

[0148] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0149] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0150] Based on the same inventive concept, corresponding to any of the above embodiments, this disclosure also provides an image acquisition device.

[0151] refer to Figure 6 The image acquisition device includes: The configuration parameter determination module 610 is configured to acquire initial imaging parameters of the imaging unit for capturing an image of the target object and evaluation parameters for evaluating the image of the target object. The first image determination module 620 is configured to control the imaging unit to capture the target object based on the initial imaging parameters to obtain a first image; The detection region determination module 630 is configured to extract a first detection region corresponding to the target object from the first image; The image parameter determination module 640 is configured to calculate the first image parameters of the first detection region; The evaluation result determination module 650 is configured to evaluate the first image based on the evaluation parameters and the first image parameters to obtain the evaluation result of the first image. The imaging parameter determination module 660 is configured to adjust the initial imaging parameters based on the first image parameters in response to the evaluation result being unsatisfactory, so as to obtain the first imaging parameters. The second image determination module 670 is configured to control the imaging unit to re-capture the target object based on the first imaging parameters to obtain a second image.

[0152] In this exemplary embodiment, the configuration parameter determination module 610 is specifically configured as follows: The initial imaging parameters of the imaging unit used to capture an image of the target object and the evaluation parameters for evaluating the image of the target object are obtained.

[0153] In this exemplary embodiment, the first image determination module 620 is specifically configured as follows: The initial imaging parameters include: initial exposure time and initial gain; based on the initial exposure time and the initial gain, the imaging unit is controlled to capture images of the target object to obtain the first image.

[0154] In this exemplary embodiment, the detection region determination module 630 is specifically configured as follows: The first image is denoised to obtain a denoised first image; the denoised first image is then binarized to obtain the first detection region.

[0155] In this exemplary embodiment, the detection region determination module 640 is specifically configured as follows: Calculate the first image parameters for the first detection region.

[0156] In this exemplary embodiment, the evaluation result determination module 650 is specifically configured as follows: The evaluation parameters include: a variance threshold and target image parameters; determining the image variance of the first image, and evaluating the image variance based on the variance threshold; adjusting the lens parameters of the imaging unit in response to the image variance being less than the variance threshold; in response to the image variance being greater than or equal to the variance threshold, the first image parameters include the average grayscale value of the image, and the target image parameters include a grayscale value threshold range; determining whether the average grayscale value is within the grayscale value threshold range; in response to determining that the average grayscale value is within the grayscale value threshold range, determining that the evaluation result meets the requirements; in response to determining that the average grayscale value is not within the grayscale value threshold range, determining that the evaluation result does not meet the requirements.

[0157] In this exemplary embodiment, the imaging parameter determination module 660 is specifically configured as follows: The evaluation parameters further include: a target scaling factor and a scaling factor threshold range; determining a first adjustment scaling factor based on the target image parameters, the first image parameters, and the target scaling factor; determining whether the first adjustment scaling factor is within the scaling factor threshold range; in response to the first adjustment scaling factor not being within the scaling factor threshold range, adjusting the initial imaging parameters according to the first adjustment scaling factor to obtain the first imaging parameters; or, The target object includes a display panel illuminated by a lighting device; the adjustment parameters of the imaging parameters of the imaging unit are obtained, the adjustment parameters including: exposure time offset and refresh rate coefficient of the lighting device; adjusting the initial imaging parameters according to the first adjustment ratio coefficient to obtain the first imaging parameter includes: adjusting the initial exposure time according to the first adjustment ratio coefficient, the refresh rate coefficient and the exposure time offset to obtain a first exposure time; determining the first imaging parameter according to the first exposure time; and adjusting the parameter value of the first exposure time to the parameter value of the maximum exposure time in response to the first exposure time being greater than the maximum exposure time.

[0158] In this exemplary embodiment, the second image determination module 670 is specifically configured as follows: Based on the first imaging parameters, the imaging unit is controlled to re-capture the target object to obtain a second image.

[0159] For ease of description, the above apparatus is described in terms of its functions, divided into various modules. Of course, in implementing this disclosure, the functions of each module can be implemented in one or more software and / or hardware.

[0160] The apparatus described above is used to implement the corresponding image acquisition method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0161] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the image acquisition method described in any of the above embodiments.

[0162] Figure 7This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0163] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0164] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0165] The input / output interface 1030 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0166] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0167] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0168] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0169] The electronic devices described above are used to implement the corresponding image acquisition methods in any of the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0170] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the image acquisition method as described in any of the above embodiments.

[0171] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0172] The aforementioned non-transitory computer-readable storage media can be any available medium or data storage device that a computer can access, including but not limited to magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0173] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the image acquisition method as described in any of the embodiments in the exemplary method section above, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0174] Based on the same inventive concept, corresponding to the image acquisition method described in any of the above embodiments, this disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to perform the image acquisition method. Corresponding to the execution entity for each step in each embodiment of the image acquisition method, the processor executing the corresponding step can belong to the corresponding execution entity.

[0175] The computer program product of the above embodiments is used to cause the computer and / or the processor to execute the image acquisition method as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0176] Those skilled in the art will recognize that embodiments of this disclosure can be implemented as a system, method, or computer program product. Therefore, this disclosure can be implemented as entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this disclosure can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0177] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (not exhaustive) of a computer-readable storage medium may include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0178] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0179] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0180] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0181] It should be understood that each block of a flowchart and / or block diagram, as well as combinations of blocks in a flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine that, when executed by a computer or other programmable data processing device, creates means for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0182] These computer program instructions may also be stored in a computer-readable medium that enables a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce a product comprising an instruction apparatus that implements the functions / operations specified in the boxes of a flowchart and / or block diagram.

[0183] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable apparatus can provide a process for implementing the functions / operations specified in the boxes of a flowchart and / or block diagram.

[0184] Furthermore, although the operations of the methods of this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. Rather, the steps depicted in the flowcharts may be executed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0185] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0186] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0187] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0188] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0189] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0190] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

[0191] While the spirit and principles of this disclosure have been described with reference to several specific embodiments, it should be understood that this disclosure is not limited to the disclosed specific embodiments, and the division of aspects does not imply that features in these aspects cannot be combined for benefit; such division is merely for convenience of expression. This disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. The scope of the appended claims is to be interpreted in the broadest sense, thereby encompassing all such modifications and equivalent structures and functions.

Claims

1. An image acquisition method, characterized in that, include: Acquire initial imaging parameters for the imaging unit used to capture an image of the target object, and evaluation parameters for evaluating the image of the target object; Based on the initial imaging parameters, the imaging unit is controlled to capture an image of the target object to obtain a first image; Extract the first detection region corresponding to the target object from the first image; Calculate the first image parameters of the first detection region; The first image is evaluated based on the evaluation parameters and the first image parameters to obtain the evaluation result of the first image; In response to the evaluation result being unsatisfactory, the initial imaging parameters are adjusted based on the first image parameters to obtain the first imaging parameters; Based on the first imaging parameters, the imaging unit is controlled to re-capture the target object to obtain a second image.

2. The method according to claim 1, characterized in that, The initial imaging parameters include: initial exposure time and initial gain; The step of controlling the imaging unit to capture an image of the target object based on the initial imaging parameters to obtain a first image includes: Based on the initial exposure time and the initial gain, the imaging unit is controlled to capture an image of the target object to obtain the first image.

3. The method according to claim 1, characterized in that, Extracting the first detection region corresponding to the target object from the first image includes: The first image is denoised to obtain a denoised first image; The first image after denoising is binarized to obtain the first detection region.

4. The method according to claim 2, characterized in that, The evaluation parameters include: variance threshold and target image parameters; The step of evaluating the first image based on the evaluation parameters and the first image parameters to obtain the evaluation result of the first image includes: Determine the image variance of the first image, and evaluate the image variance based on the variance threshold; In response to the image variance being less than the variance threshold, the lens parameters of the imaging unit are adjusted; In response to the image variance being greater than or equal to the variance threshold, the first image parameters are evaluated based on the target image parameters to obtain the evaluation result.

5. The method according to claim 4, characterized in that, The first image parameter includes the average grayscale value of the image, and the target image parameter includes a grayscale value threshold range; The evaluation of the first image parameters based on the target image parameters to obtain the evaluation result includes: Determine whether the average grayscale value is within the grayscale value threshold range; In response to determining that the average grayscale value is within the grayscale value threshold range, the evaluation result is determined to meet the requirements; In response to determining that the average grayscale value is not within the grayscale value threshold range, the evaluation result is determined to be non-compliant.

6. The method according to claim 4 or 5, characterized in that, The evaluation parameters also include: the target scaling factor and the scaling factor threshold range; The step of adjusting the initial imaging parameters based on the first image parameters to obtain the first imaging parameters includes: Based on the target image parameters, the first image parameters, and the target scaling factor, a first adjustment scaling factor is determined; Determine whether the first adjustment ratio coefficient is within the range of the ratio coefficient threshold; In response to the first adjustment ratio coefficient not being within the range of the ratio coefficient threshold, the initial imaging parameters are adjusted according to the first adjustment ratio coefficient to obtain the first imaging parameters.

7. The method according to claim 6, characterized in that, The target object includes a display panel that is lit up by a lighting device; The method further includes: obtaining adjustment parameters of the imaging parameters of the imaging unit, the adjustment parameters including: exposure time offset and refresh rate coefficient of the lighting device; The step of adjusting the initial imaging parameters according to the first adjustment ratio coefficient to obtain the first imaging parameters includes: The initial exposure time is adjusted according to the first adjustment ratio coefficient, the refresh rate coefficient, and the exposure time offset to obtain the first exposure time; The first imaging parameters are determined based on the first exposure time.

8. The method according to claim 7, characterized in that, The method further includes: Extract the second detection region corresponding to the target object from the second image; Calculate the second image parameters of the second detection region; The second image is evaluated based on the evaluation parameters and the second image parameters to obtain the evaluation result of the second image; In response to the evaluation result being unsatisfactory, the first imaging parameters are adjusted based on the second image parameters to obtain the second imaging parameters; Based on the second imaging parameters, the imaging unit is controlled to re-capture the target object to obtain a third image.

9. The method according to claim 8, characterized in that, The adjustment parameters also include gain offset; The step of adjusting the first imaging parameters based on the second image parameters to obtain the second imaging parameters includes: Based on the target image parameters, the second image parameters, and the target scaling factor, a second adjustment scaling factor is determined; Determine whether the second adjustment ratio is within the range of the ratio threshold; In response to the second adjustment ratio coefficient not being within the ratio coefficient threshold range, the first exposure time is adjusted according to the second adjustment ratio coefficient, the refresh rate coefficient, and the exposure time offset to obtain the second exposure time; In response to the first adjustment scaling factor being greater than the target scaling factor and the second adjustment scaling factor being less than the target scaling factor, or the first adjustment scaling factor being less than the target scaling factor and the second adjustment scaling factor being greater than the target scaling factor, the initial gain is adjusted according to the gain offset, the target scaling factor, the target image parameters and the second image parameters to obtain a first gain. The second imaging parameters are determined based on the second exposure time and the first gain.

10. The method according to claim 7, characterized in that, The evaluation parameters also include: maximum exposure time, and the method further includes: In response to the first exposure time being greater than the maximum exposure time, the parameter value of the first exposure time is adjusted to the parameter value of the maximum exposure time.

11. The method according to claim 9, characterized in that, The adjustment parameter further includes: the maximum number of iterations; the method further includes: In response to the maximum number of iterations reached when the imaging parameters of the imaging unit are adjusted, corresponding adjustment information is output.

12. An image acquisition device, characterized in that, include: The configuration parameter determination module is configured to acquire initial imaging parameters of the imaging unit for capturing an image of the target object and evaluation parameters for evaluating the image of the target object; The first image determination module is configured to control the imaging unit to capture a first image of the target object based on the initial imaging parameters. The detection region determination module is configured to extract a first detection region corresponding to the target object from the first image; The image parameter determination module is configured to calculate the first image parameters of the first detection region; The evaluation result determination module is configured to evaluate the first image based on the evaluation parameters and the first image parameters to obtain the evaluation result of the first image; An imaging parameter determination module is configured to adjust the initial imaging parameters based on the first image parameters in response to the evaluation result being unsatisfactory, so as to obtain the first imaging parameters. The second image determination module is configured to control the imaging unit to re-capture the target object based on the first imaging parameters to obtain a second image.

13. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1 to 11.

14. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to perform the method of any one of claims 1 to 11.

15. A computer program product, characterized in that, It includes computer program instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 11.

Images