Gate channel target identification and detection method and system

Abstract

The invention relates to the technical field of gate channel target image recognition, and particularly discloses a gate channel target recognition detection method and system, and the method comprises the steps: collecting image data, carrying out the image preprocessing steps, such as illumination compensation and noise removal, extracting the brightness features and texture features in an image, and calculating the visibility coefficient of a target region. According to the method, high identifiability of the target is kept under various illumination conditions, the final identification accuracy of the target in different environments is evaluated by comprehensively analyzing the visibility coefficient and the identification confidence of the target, so that the image preprocessing and target identification algorithms are dynamically adjusted, the target can be rapidly and accurately identified, and the identification accuracy of the target is improved. The target identification efficiency in the gate channel is improved, the high efficiency and robustness of the system under the complex illumination condition are ensured, and the method has practical value and application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition of gate channel targets, and in particular to a gate channel target recognition and detection method and system. Background Art

[0002] With the development of intelligent transportation systems, gate channels, as key access management areas, are widely used in places such as subways, airports, and scenic spots to achieve personnel identity authentication and access management. In order to ensure traffic safety and efficiency, target recognition technology has been introduced into the gate system to accurately identify passers-by. However, target recognition in gate channels faces a series of challenges such as lighting changes and environmental interference. Especially under strong light, backlight or low light conditions, traditional target recognition methods are difficult to guarantee efficient and accurate recognition results. This makes the performance of existing image recognition systems under complex lighting conditions greatly affected and unable to meet actual application requirements.

[0003] The prior art has the following deficiencies: Although the existing gate target recognition system adopts a variety of image processing methods, such as illumination compensation, noise removal and dynamic range adjustment, these technologies are often unable to fully cope with the challenges in different lighting environments, especially in the case of strong lighting differences or rapid changes, the recognition effect of the system is unstable. In addition, most of the existing target recognition algorithms rely on static feature extraction, which makes it difficult to effectively evaluate the recognizability of the image, and fail to fully consider the changes in the visibility of the target in the image and the impact of different lighting conditions. The existing methods lack a systematic evaluation method for the accuracy and robustness of target recognition, which leads to the problem of misidentification or missed recognition in complex scenes. Therefore, there is an urgent need for a new technical solution that can dynamically adjust the image preprocessing, feature extraction and target recognition algorithms to ensure that the target recognition task can be completed stably and accurately under various lighting changes. Summary of the invention

[0004] The object of the present invention is to provide a gate channel target recognition detection method and system to solve the above-mentioned problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A gate channel target recognition and detection method comprises the following steps: S1: Collect gate channel image data through the camera, and perform illumination compensation, noise removal and high dynamic range imaging processing on the image to improve image quality and adapt to target recognition under different lighting conditions; S2: extracting brightness features and texture features of the target area from the preprocessed image, wherein the brightness features are obtained by brightness contrast analysis of the image, and the texture features are obtained by a texture analysis algorithm to describe the brightness difference and texture clarity of the target area; S3: Based on the extracted brightness features and texture features, the target visibility coefficient of the image is calculated. The target visibility coefficient is used to evaluate the degree of recognition of the target area under different lighting conditions and ensure the visibility of the target area under changing lighting conditions; S4: Detecting the targets in the image using a target recognition algorithm, calculating the recognition confidence of each target, wherein the confidence represents the probability that the target is correctly recognized, and evaluating the consistency of the target recognition results; S5: Comprehensively analyze the visibility and recognition confidence of the target to obtain a comprehensive recognition accuracy score, which is used to evaluate the final recognition accuracy of the target under different lighting conditions; S6: Based on the comprehensive analysis results, the image preprocessing, feature extraction and target recognition algorithms are dynamically adjusted to ensure fast and accurate target recognition under different lighting conditions.

[0006] As a further solution of the present invention: the step of extracting the brightness features and texture features of the target area from the preprocessed image specifically includes: Through multi-scale analysis technology, the brightness information of the image at different resolutions is extracted; Use Gaussian pyramid and Laplacian pyramid to decompose the image at multiple scales and extract the brightness contrast information of the image at different scales; Based on local contrast analysis, the brightness difference between the target area and the background area is calculated to obtain the brightness characteristics of the target area; Use LBP algorithm to extract local texture information from the image and obtain the texture coding of the target area; The image is filtered in frequency and direction by Gabor filter to extract the texture structure features of the target area. The overall value of energy features and the overall value of phase features of each filtered image are calculated to obtain the texture features of the target area.

[0007] As a further solution of the present invention: the target visibility coefficient of the image is calculated based on the extracted brightness features and texture features, specifically including: Analyze the brightness difference between the target area and the background area through the local contrast enhancement algorithm , and calculate the brightness feature of the target area based on the brightness difference between the target area and the background area , the calculation expression is: ; Based on the local contrast analysis results, the brightness characteristic index of the target area is calculated, and the calculation expression is: ; in, Indicates the brightness of the target area, Indicates the target area, Indicates the brightness of the background area, represents the local neighborhood of the target area, represents a constant to avoid division by zero, Represents the brightness difference, represents the brightness characteristic index, Indicates the target area The pixel position in the surrounding neighborhood area; Use Gabor filter to extract texture features of the image, including energy features and phase characteristics , and calculate the total value of the filtered image energy feature and phase feature. The calculation expression is: ; ; ; ; in, represents the response of the Gabor filter, Represents energy characteristics, Represents the phase characteristics, The overall value of energy characteristics, represents the overall value of the phase characteristic, represents the angle variable, represents the standard deviation of the Gaussian function; Calculate the texture feature index, the calculation expression is: ; In the formula, represents the texture feature index; By combining the energy feature and the phase feature, the texture feature index is calculated. The calculation expression is: The comprehensive brightness feature index and the texture feature index are fused to obtain the target visibility coefficient. The calculation expression is: ; in, is the weight coefficient of brightness, and the calculation expression is: ; in, is the weight coefficient of the texture, and the calculation expression is: ; in, To control the adjustment parameters of brightness and texture weight, Represents the target visibility coefficient.

[0008] As a further solution of the present invention: the detection of the target in the image by using the target recognition algorithm and the calculation of the recognition confidence of each target specifically include: Extract features of the target area , including brightness features and texture features; Locate the target area through the target detection algorithm and background area ; The prior probability of the target is calculated based on the target features and training data. The calculation expression is: ; In the formula, represents the prior probability of the target, represents the first Features, Represents the characteristics of the background area, Indicates the target area, Indicates the first target categories, Indicates that the category is The prior probability of is the sample size; Calculate the likelihood function of the target area features, and the calculation expression is: , in, represents the likelihood function of the target region features, is the dimension of the feature, It is Features, Indicates The mean of the features, Indicates The standard deviation of the feature, Indicates the category of the target feature; Calculate the posterior probability of the target, the calculation expression is: ; In the formula, represents the posterior probability, represents the total observation probability; Calculate the confidence coefficient of target recognition, the calculation expression is: ; In the formula, represents the confidence coefficient of target recognition, Indicates the preset adjustment coefficient, Represents the logarithm of the base of a natural number.

[0009] As a further solution of the present invention: the consistency of the evaluation target recognition result specifically includes: It is determined whether the confidence coefficient of the target recognition result is greater than or equal to a preset threshold. If so, it means that the corresponding target recognition results are consistent. If not, it means that the corresponding target recognition results are inconsistent.

[0010] As a further solution of the present invention: the visibility and recognition confidence of the target are comprehensively analyzed to obtain a comprehensive recognition accuracy score, which specifically includes: Obtain the target visibility coefficient and confidence coefficient of the gate channel image data, perform normalized calculation on the target visibility coefficient and confidence coefficient, and calculate a comprehensive recognition accuracy score for evaluating the accuracy of image recognition; The calculation expression of the normalized calculation process is: ; In the formula, represents the comprehensive recognition accuracy score, and Indicates the preset scale factor, represents the confidence coefficient, Represents the target visibility coefficient.

[0011] As a further solution of the present invention: the final recognition accuracy of the evaluation target under different lighting conditions specifically includes: It is determined whether the comprehensive recognition accuracy score of the target under different lighting conditions is greater than or equal to the preset threshold. If so, it means that the corresponding final recognition is accurate; if not, it means that the corresponding final recognition is inaccurate.

[0012] As a further solution of the present invention: the method of dynamically adjusting the image preprocessing, feature extraction and target recognition algorithms based on the comprehensive analysis results specifically includes: If the target visibility coefficient is lower than the preset threshold, the intensity of the illumination compensation algorithm is enhanced to automatically adjust the image brightness and contrast to improve the clarity of the target area; If the recognition confidence is lower than the preset threshold, the brightness difference of the target area is dynamically adjusted by increasing the brightness contrast; When the texture information of an image is inaccurate, the frequency response of the Gabor filter is increased and the texture feature extraction is optimized to improve the discrimination of the target texture.

[0013] A gate channel target recognition and detection system, comprising: A data acquisition module, which collects gate channel image data through a camera and performs illumination compensation, noise removal and high dynamic range imaging processing on the image to improve image quality and adapt to target recognition under different lighting conditions; A feature extraction module, wherein the feature extraction module extracts brightness features and texture features of the target area from the preprocessed image, wherein the brightness features are obtained by brightness contrast analysis of the image, and the texture features are obtained by a texture analysis algorithm to describe brightness differences and texture clarity of the target area; A target visibility coefficient calculation module, wherein the target visibility coefficient calculation module calculates the target visibility coefficient of the image based on the extracted brightness features and texture features, and the target visibility coefficient is used to evaluate the identifiability of the target area under different lighting conditions to ensure the visibility of the target area under changing lighting conditions; A confidence recognition module, which detects targets in the image using a target recognition algorithm, calculates the recognition confidence of each target, wherein the confidence represents the probability that the target is correctly recognized, and evaluates the consistency of the target recognition results; A comprehensive evaluation module, which comprehensively analyzes the visibility and recognition confidence of the target to obtain a comprehensive recognition accuracy score for evaluating the final recognition accuracy of the target under different lighting conditions; A dynamic control module, based on the comprehensive analysis results, dynamically adjusts the image preprocessing, feature extraction and target recognition algorithms to ensure fast and accurate target recognition under different lighting conditions.

[0014] Beneficial effects of the present invention: (1) The present invention comprehensively optimizes the image quality under different lighting conditions by comprehensively using illumination compensation, noise removal and high dynamic range imaging (HDR) technology. First, by dynamically adjusting the exposure time and gain of the camera, it automatically adapts to changes in ambient illumination, avoiding image distortion due to overexposure or underexposure, thereby ensuring that the image can maintain clear details in complex environments such as strong light and backlight, and avoiding the difficulty of target recognition caused by illumination differences. At the same time, by adopting high dynamic range imaging technology, by synthesizing images with different exposures, the detail retention of dark and bright areas in the image is enhanced, thereby improving the visual quality of the overall image. In addition, in the image preprocessing stage, brightness contrast analysis and advanced texture analysis algorithms (such as local binary pattern LBP and Gabor filtering) are combined to accurately extract the brightness features and texture features of the target area, and then the image texture features are optimized by the local contrast enhancement algorithm and Gabor filter, so that the target area can still maintain a high visibility coefficient under the condition of illumination changes, thereby improving the clarity and recognizability of the target area. This combination of multi-dimensional image processing and feature extraction methods improves image quality, lays a solid foundation for subsequent target recognition, and effectively copes with challenges under different lighting conditions; (2) The target visibility coefficient accurately combines the brightness contrast and texture features of the image, comprehensively evaluates the identifiability of the target under different lighting conditions, and thus provides key information support for subsequent target detection. At the same time, the target recognition algorithm accurately locates the target based on deep learning and training data, and calculates the recognition confidence coefficient, which reflects the probability that the target is correctly recognized. By combining the target visibility coefficient with the recognition confidence coefficient, the system can dynamically adjust the image preprocessing and target recognition algorithm to ensure that when the lighting environment changes, target recognition can not only be carried out quickly but also maintain high accuracy. This comprehensive analysis method enables the system to efficiently and accurately complete the target recognition task under complex lighting conditions, greatly improving the efficiency and robustness of target recognition in the gate channel, not only reducing the misrecognition rate, but also ensuring the stability and efficiency of the entire system, and adapting to the actual application needs in various complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described below in conjunction with the accompanying drawings.

[0016] Figure 1 It is a flowchart of the specific steps of a gate channel target recognition and detection method of the present invention; Figure 2 It is a flow chart of a gate channel target recognition and detection system in the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1 As shown, the present invention is a gate channel target recognition and detection method, comprising the following steps: S1: Collect gate channel image data through the camera, and perform illumination compensation, noise removal and high dynamic range imaging processing on the image to improve image quality and adapt to target recognition under different lighting conditions; S2: extracting brightness features and texture features of the target area from the preprocessed image, wherein the brightness features are obtained by brightness contrast analysis of the image, and the texture features are obtained by a texture analysis algorithm to describe the brightness difference and texture clarity of the target area; S3: Based on the extracted brightness features and texture features, the target visibility coefficient of the image is calculated. The target visibility coefficient is used to evaluate the degree of recognition of the target area under different lighting conditions and ensure the visibility of the target area under changing lighting conditions; S4: Detecting the targets in the image using a target recognition algorithm, calculating the recognition confidence of each target, wherein the confidence represents the probability that the target is correctly recognized, and evaluating the consistency of the target recognition results; S5: Comprehensively analyze the visibility and recognition confidence of the target to obtain a comprehensive recognition accuracy score, which is used to evaluate the final recognition accuracy of the target under different lighting conditions; S6: Based on the comprehensive analysis results, the image preprocessing, feature extraction and target recognition algorithms are dynamically adjusted to ensure fast and accurate target recognition under different lighting conditions.

[0019] In S1, the camera collects gate channel image data, and performs illumination compensation, noise removal, and high dynamic range imaging processing on the image to improve image quality and adapt to target recognition under different lighting conditions, including: During the target recognition process of the gate channel, the camera is first used to collect real-time images of the channel. The camera adopts a high-resolution configuration to ensure that the details of the target area can be clearly captured in complex lighting environments. During the acquisition process, the camera's exposure time and gain settings will be automatically adjusted according to lighting changes to ensure that the image will not be distorted due to overexposure or underexposure. At the same time, the dynamic range control configured in the camera can provide a relatively balanced image brightness under different lighting conditions, so that the image details can be restored in the subsequent processing stage.

[0020] The captured images are immediately preprocessed, including illumination compensation, noise removal, and high dynamic range imaging (HDR) processing. Illumination compensation adjusts the brightness and contrast of the image to eliminate image deviations caused by uneven illumination, especially improving image quality under strong light or backlight conditions. Noise removal uses filtering algorithms, such as Gaussian filtering or bilateral filtering, to remove noise caused by sensor noise, motion blur, or environmental interference. High dynamic range imaging processing reconstructs the high dynamic range of the image by merging multiple images with different exposure levels, ensuring that detail information is retained in areas that are too dark or too bright, providing a clear image basis for subsequent target recognition.

[0021] In S2, the brightness feature and texture feature of the target area are extracted from the preprocessed image, wherein the brightness feature is obtained by brightness contrast analysis of the image, and the texture feature is obtained by a texture analysis algorithm to describe the brightness difference and texture clarity of the target area, specifically including: Through multi-scale analysis technology, the brightness information of the image at different resolutions is extracted; Use Gaussian pyramid and Laplacian pyramid to decompose the image at multiple scales and extract the brightness contrast information of the image at different scales; Based on local contrast analysis, the brightness difference between the target area and the background area is calculated to obtain the brightness characteristics of the target area; Use LBP algorithm to extract local texture information from the image and obtain the texture coding of the target area; The image is filtered in frequency and direction by Gabor filter to extract the texture structure features of the target area. The overall value of energy features and the overall value of phase features of each filtered image are calculated to obtain the texture features of the target area.

[0022] In S3, based on the extracted brightness features and texture features, the target visibility coefficient of the image is calculated. The target visibility coefficient is used to evaluate the degree of recognition of the target area under different lighting conditions and ensure the visibility of the target area under changing lighting conditions. Specifically, it includes: Analyze the brightness difference between the target area and the background area through the local contrast enhancement algorithm , and calculate the brightness feature of the target area based on the brightness difference between the target area and the background area , the calculation expression is: ; Based on the local contrast analysis results, the brightness characteristic index of the target area is calculated, and the calculation expression is: ; in, Indicates the brightness of the target area, Indicates the target area, Indicates the brightness of the background area, represents the local neighborhood of the target area, represents a constant to avoid division by zero, Represents the brightness difference, represents the brightness characteristic index, Indicates the target area The pixel position in the surrounding neighborhood area; Use Gabor filter to extract texture features of the image, including energy features and phase characteristics , and calculate the total value of the filtered image energy feature and phase feature. The calculation expression is: ; ; ; ; in, represents the response of the Gabor filter, Represents energy characteristics, Represents the phase characteristics, The overall value of energy characteristics, represents the overall value of the phase characteristic, represents the angle variable, represents the standard deviation of the Gaussian function; Calculate the texture feature index, the calculation expression is: ; In the formula, represents the texture feature index; By combining the energy feature and the phase feature, the texture feature index is calculated. The calculation expression is: The comprehensive brightness feature index and the texture feature index are fused to obtain the target visibility coefficient. The calculation expression is: ; in, is the weight coefficient of brightness, and the calculation expression is: ; in, is the weight coefficient of the texture, and the calculation expression is: ; in, To control the adjustment parameters of brightness and texture weight, Represents the target visibility coefficient.

[0023] Determine whether the target visibility coefficient of the target area under different lighting conditions is greater than or equal to a preset threshold. If so, it means that the image of the corresponding target area is recognizable. If not, the corresponding target area is affected by the lighting and cannot be recognized. It should be noted that the target visibility coefficient reflects whether the target area of ​​image recognition can be clearly identified, and when the value of the target visibility coefficient is larger, the corresponding image recognition is more difficult.

[0024] In S4, the target in the image is detected using a target recognition algorithm, the recognition confidence of each target is calculated, and the confidence represents the probability that the target is correctly recognized, and the consistency of the target recognition result is evaluated, which specifically includes: Extract features of the target area , including brightness features and texture features; Locate the target area through the target detection algorithm and background area ; The prior probability of the target is calculated based on the target features and training data. The calculation expression is: ; In the formula, represents the prior probability of the target, represents the first Features, Represents the characteristics of the background area, Indicates the target area, Indicates the first target categories, Indicates that the category is The prior probability of is the sample size; Calculate the likelihood function of the target area features, and the calculation expression is: , in, represents the likelihood function of the target region features, is the dimension of the feature, It is Features, Indicates The mean of the features, Indicates The standard deviation of the feature, Indicates the category of the target feature; Calculate the posterior probability of the target, the calculation expression is: ; In the formula, represents the posterior probability, represents the total observation probability; Calculate the confidence coefficient of target recognition, the calculation expression is: ; In the formula, represents the confidence coefficient of target recognition, Indicates the preset adjustment coefficient, Represents the logarithm of the base of a natural number.

[0025] Determine whether the confidence coefficient of the target recognition result is greater than or equal to a preset threshold. If so, it means that the corresponding target recognition results are consistent. If not, it means that the corresponding target recognition results are inconsistent. It should be noted that the confidence coefficient reflects the consistency of the recognition results in the target recognition process in the target area, and the larger the value of the confidence coefficient, the higher the recognition consistency.

[0026] In S5, the visibility and recognition confidence of the target are comprehensively analyzed to obtain a comprehensive recognition accuracy score, which is used to evaluate the final recognition accuracy of the target under different lighting conditions, including: Obtain the target visibility coefficient and confidence coefficient of the gate channel image data, perform normalized calculation on the target visibility coefficient and confidence coefficient, and calculate a comprehensive recognition accuracy score for evaluating the accuracy of image recognition; The calculation expression of the normalized calculation process is: ; In the formula, represents the comprehensive recognition accuracy score, and Indicates the preset scale factor, represents the confidence coefficient, Represents the target visibility coefficient.

[0027] It should be noted that the comprehensive recognition accuracy score reflects the accuracy of target recognition in the image recognition process of the gate channel, and the larger the comprehensive recognition accuracy score, the more accurate the corresponding recognition process.

[0028] In S6, based on comprehensive analysis results, image preprocessing, feature extraction and target recognition algorithms are dynamically adjusted to ensure fast and accurate target recognition under different lighting conditions, including: Automatically adjust the illumination compensation parameters, noise removal methods, and high dynamic range imaging processing in the image preprocessing step according to the changes in the comprehensive recognition accuracy score and the target visibility coefficient; When the target visibility coefficient is lower than the preset threshold, the intensity of the illumination compensation algorithm is enhanced to automatically adjust the image brightness and contrast to improve the clarity of the target area; According to the evaluation results of the target visibility coefficient and confidence coefficient, the local contrast analysis parameters in the brightness feature extraction are automatically adjusted. Specifically: when the recognition confidence is low, the brightness difference of the target area is dynamically adjusted by increasing the brightness contrast; When the texture information of the image is inaccurate, the frequency response of the Gabor filter is increased and the texture feature extraction is optimized to improve the discrimination of the target texture; After the comprehensive recognition accuracy score is calculated, the image preprocessing, feature extraction and target recognition algorithms are dynamically adjusted through the feedback mechanism to achieve self-update of the image preprocessing, feature extraction and target recognition algorithms.

[0029] See also Figure 2 As shown, a gate channel target recognition and detection system includes: A data acquisition module, which collects gate channel image data through a camera and performs illumination compensation, noise removal and high dynamic range imaging processing on the image to improve image quality and adapt to target recognition under different lighting conditions; A feature extraction module, wherein the feature extraction module extracts brightness features and texture features of the target area from the preprocessed image, wherein the brightness features are obtained by brightness contrast analysis of the image, and the texture features are obtained by a texture analysis algorithm to describe brightness differences and texture clarity of the target area; A target visibility coefficient calculation module, wherein the target visibility coefficient calculation module calculates the target visibility coefficient of the image based on the extracted brightness features and texture features, and the target visibility coefficient is used to evaluate the identifiability of the target area under different lighting conditions to ensure the visibility of the target area under changing lighting conditions; A confidence recognition module, which detects targets in the image using a target recognition algorithm, calculates the recognition confidence of each target, wherein the confidence represents the probability that the target is correctly recognized, and evaluates the consistency of the target recognition results; A comprehensive evaluation module, which comprehensively analyzes the visibility and recognition confidence of the target to obtain a comprehensive recognition accuracy score for evaluating the final recognition accuracy of the target under different lighting conditions; A dynamic control module, based on the comprehensive analysis results, dynamically adjusts the image preprocessing, feature extraction and target recognition algorithms to ensure fast and accurate target recognition under different lighting conditions.

[0030] The working principle of the present invention is as follows: the high-resolution image of the gate channel is collected in real time by the camera, and the exposure and gain control are dynamically adjusted to ensure the stability of the image quality in complex lighting environments. The collected images are processed by illumination compensation, noise removal and high dynamic range imaging to improve the contrast and detail clarity of the image, ensuring that the target area can be clearly identified under different lighting conditions such as strong light and backlight. Subsequently, the brightness features and texture features of the target area are extracted by multi-scale analysis, local contrast analysis and Gabor filtering, and the visibility coefficient of the target is calculated, which is used to evaluate the degree of recognition of the target under different lighting conditions. At the same time, the target is detected by the target recognition algorithm and its recognition confidence is calculated to reflect the consistency of the target recognition result. The target visibility coefficient and the confidence coefficient are comprehensively analyzed to obtain a comprehensive recognition accuracy score, which is used to evaluate the final recognition accuracy of the target under different lighting conditions. According to the comprehensive analysis results, the parameters of the image preprocessing, feature extraction and target recognition algorithms are dynamically adjusted to achieve fast and accurate target recognition. The present invention can optimize the image processing process under complex lighting conditions, improve the accuracy of target recognition, has strong adaptability and practicality, and is widely applicable to target recognition tasks in intelligent gate systems.

[0031] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0032] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0033] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0034] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0035] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A gate channel target recognition and detection method, characterized in that: The following steps are involved: S1: Collect gate channel image data through the camera, and perform illumination compensation, noise removal and high dynamic range imaging processing on the image to improve image quality and adapt to target recognition under different lighting conditions; S2: extracting brightness features and texture features of the target area from the preprocessed image, wherein the brightness features are obtained by brightness contrast analysis of the image, and the texture features are obtained by a texture analysis algorithm to describe the brightness difference and texture clarity of the target area; S3: Based on the extracted brightness features and texture features, the target visibility coefficient of the image is calculated. The target visibility coefficient is used to evaluate the degree of recognition of the target area under different lighting conditions and ensure the visibility of the target area under changing lighting conditions; S4: Detecting the targets in the image using a target recognition algorithm, calculating the recognition confidence of each target, wherein the confidence represents the probability that the target is correctly recognized, and evaluating the consistency of the target recognition results; S5: Comprehensively analyze the visibility and recognition confidence of the target to obtain a comprehensive recognition accuracy score, which is used to evaluate the final recognition accuracy of the target under different lighting conditions; S6: Based on the comprehensive analysis results, the image preprocessing, feature extraction and target recognition algorithms are dynamically adjusted to ensure fast and accurate target recognition under different lighting conditions.

2. A gate channel target recognition and detection method according to claim 1, characterized in that: The step of extracting the brightness feature and the texture feature of the target area from the preprocessed image specifically includes: Through multi-scale analysis technology, the brightness information of the image at different resolutions is extracted; Use Gaussian pyramid and Laplacian pyramid to decompose the image at multiple scales and extract the brightness contrast information of the image at different scales; Based on local contrast analysis, the brightness difference between the target area and the background area is calculated to obtain the brightness characteristics of the target area; Use LBP algorithm to extract local texture information from the image and obtain the texture coding of the target area; The image is filtered in frequency and direction by Gabor filter to extract the texture structure features of the target area. The overall value of energy features and the overall value of phase features of each filtered image are calculated to obtain the texture features of the target area.

3. A gate channel target recognition and detection method according to claim 1, characterized in that: The step of calculating the target visibility coefficient of the image based on the extracted brightness features and texture features specifically includes: Analyze the brightness difference between the target area and the background area through the local contrast enhancement algorithm , and calculate the brightness feature of the target area based on the brightness difference between the target area and the background area , the calculation expression is: ; Based on the local contrast analysis results, the brightness characteristic index of the target area is calculated, and the calculation expression is: ; in, Indicates the brightness of the target area, Indicates the target area, Indicates the brightness of the background area, represents the local neighborhood of the target area, represents a constant to avoid division by zero, Represents the brightness difference, represents the brightness characteristic index, Indicates the target area The pixel position in the surrounding neighborhood area; Use Gabor filter to extract texture features of the image, including energy features and phase characteristics , and calculate the total value of the filtered image energy feature and phase feature. The calculation expression is: ; ; ; ; in, represents the response of the Gabor filter, Represents energy characteristics, Represents the phase characteristics, The overall value of energy characteristics, represents the overall value of the phase characteristic, represents the angle variable, represents the standard deviation of the Gaussian function; Calculate the texture feature index, the calculation expression is: ; In the formula, represents the texture feature index; By combining the energy feature and the phase feature, the texture feature index is calculated. The calculation expression is: The comprehensive brightness feature index and the texture feature index are fused to obtain the target visibility coefficient. The calculation expression is: ; in, is the weight coefficient of brightness, and the calculation expression is: ; in, is the weight coefficient of the texture, and the calculation expression is: ; in, To control the adjustment parameters of brightness and texture weight, Represents the target visibility coefficient.

4. A gate channel target recognition and detection method according to claim 1, characterized in that: The evaluation of the identifiability of the target area under different lighting conditions specifically includes: It is determined whether the target visibility coefficient of the target area under different lighting conditions is greater than or equal to a preset threshold. If so, it means that the image of the corresponding target area is recognizable. If not, the corresponding target area is affected by the lighting and cannot be recognized.

5. A gate channel target recognition and detection method according to claim 1, characterized in that: The target recognition algorithm is used to detect the target in the image and calculate the recognition confidence of each target, which specifically includes: Extract features of the target area , including brightness features and texture features; Locate the target area through the target detection algorithm and background area ; The prior probability of the target is calculated based on the target features and training data. The calculation expression is: ; In the formula, represents the prior probability of the target, represents the first feature vector of the target region Features, Represents the characteristics of the background area, Indicates the target area, Indicates the first target categories, Indicates that the category is The prior probability of is the sample size; Calculate the likelihood function of the target area features, and the calculation expression is: , in, represents the likelihood function of the target region features, is the dimension of the feature, It is Features, Indicates The mean of the features, Indicates The standard deviation of the feature, Indicates the category of the target feature; Calculate the posterior probability of the target, the calculation expression is: ; In the formula, represents the posterior probability, represents the total observation probability; Calculate the confidence coefficient of target recognition, the calculation expression is: ; In the formula, represents the confidence coefficient of target recognition, Indicates the preset adjustment coefficient, Represents the logarithm of the base of a natural number.

6. A gate channel target recognition and detection method according to claim 1, characterized in that: The consistency of the evaluation target recognition result specifically includes: It is determined whether the confidence coefficient of the target recognition result is greater than or equal to a preset threshold. If so, it means that the corresponding target recognition results are consistent. If not, it means that the corresponding target recognition results are inconsistent.

7. A gate channel target recognition and detection method according to claim 1, characterized in that: The target visibility and recognition confidence are comprehensively analyzed to obtain a comprehensive recognition accuracy score, which specifically includes: Obtain the target visibility coefficient and confidence coefficient of the gate channel image data, perform normalized calculation on the target visibility coefficient and confidence coefficient, and calculate a comprehensive recognition accuracy score for evaluating the accuracy of image recognition; The calculation expression of the normalized calculation process is: ; In the formula, represents the comprehensive recognition accuracy score, and Indicates the preset scale factor, represents the confidence coefficient, Represents the target visibility coefficient.

8. A gate channel target recognition and detection method according to claim 1, characterized in that: The final recognition accuracy of the evaluation target under different lighting conditions specifically includes: It is determined whether the comprehensive recognition accuracy score of the target under different lighting conditions is greater than or equal to the preset threshold. If so, it means that the corresponding final recognition is accurate; if not, it means that the corresponding final recognition is inaccurate.

9. A gate channel target recognition and detection method according to claim 1, characterized in that: Based on the comprehensive analysis results, the image preprocessing, feature extraction and target recognition algorithms are dynamically adjusted, specifically including: If the target visibility coefficient is lower than the preset threshold, the intensity of the illumination compensation algorithm is enhanced to automatically adjust the image brightness and contrast to improve the clarity of the target area; If the recognition confidence is lower than the preset threshold, the brightness difference of the target area is dynamically adjusted by increasing the brightness contrast; When the texture information of an image is inaccurate, the frequency response of the Gabor filter is increased and the texture feature extraction is optimized to improve the discrimination of the target texture.

10. A gate channel target recognition and detection system, characterized in that: A gate channel target recognition and detection method as claimed in any one of claims 1 to 9, comprising: A data acquisition module, which collects gate channel image data through a camera and performs illumination compensation, noise removal and high dynamic range imaging processing on the image to improve image quality and adapt to target recognition under different lighting conditions; A feature extraction module, wherein the feature extraction module extracts brightness features and texture features of the target area from the preprocessed image, wherein the brightness features are obtained by brightness contrast analysis of the image, and the texture features are obtained by a texture analysis algorithm to describe brightness differences and texture clarity of the target area; A target visibility coefficient calculation module, wherein the target visibility coefficient calculation module calculates the target visibility coefficient of the image based on the extracted brightness features and texture features, and the target visibility coefficient is used to evaluate the identifiability of the target area under different lighting conditions to ensure the visibility of the target area under changing lighting conditions; A confidence recognition module, which detects targets in the image using a target recognition algorithm, calculates the recognition confidence of each target, wherein the confidence represents the probability that the target is correctly recognized, and evaluates the consistency of the target recognition results; A comprehensive evaluation module, which comprehensively analyzes the visibility and recognition confidence of the target to obtain a comprehensive recognition accuracy score for evaluating the final recognition accuracy of the target under different lighting conditions; A dynamic control module, based on the comprehensive analysis results, dynamically adjusts the image preprocessing, feature extraction and target recognition algorithms to ensure fast and accurate target recognition under different lighting conditions.

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