Automatic gate control system and method based on computer vision

Abstract

The present application relates to the field of barrier gate control, and specifically discloses a barrier gate automatic control system and method based on computer vision. The system first obtains vehicle images at the barrier gate entrance collected by a visual sensor, then uses deep learning technology to perform feature extraction and association analysis, and finally obtains classification results through a classifier to determine whether the vehicle at the barrier gate entrance is an authorized vehicle, thereby sending a corresponding control signal to the barrier gate, thereby reducing the need for manual operation and inspection, improving traffic efficiency, avoiding congestion, and enhancing user experience and satisfaction.

Description

Technical Field

[0001] The present application relates to the field of barrier gate control, and more specifically, to a barrier gate automatic control system and method based on computer vision. Background Art

[0002] Barrier gates are automated devices widely used in areas such as parking lots, residential entrances, and industrial park entrances to manage vehicle access. Their primary function is to control the movement of vehicles by raising or lowering a lever, thereby ensuring that only authorized vehicles can pass.

[0003] With technological advancements, parking management systems are becoming increasingly intelligent, with access control systems being particularly crucial. Currently, many systems still rely on manual management to identify vehicles and control the raising and lowering of barriers. This approach is not only inefficient but also prone to management errors, which can negatively impact user experience and satisfaction.

[0004] Therefore, a computer vision-based automatic gate control system and method are desired. Summary of the Invention

[0005] The present application is proposed to address the above-mentioned technical problems. The embodiments of the present application provide a computer vision-based barrier gate automatic control system and method. The system first acquires images of vehicles at the barrier gate entrance collected by a visual sensor, then uses deep learning technology to perform feature extraction and association analysis. Finally, a classifier is used to obtain a classification result to determine whether the vehicle at the barrier gate entrance is an authorized vehicle, thereby issuing a corresponding control signal to the barrier gate. This reduces the need for manual operation and inspection, improves traffic efficiency, avoids congestion, and enhances user experience and satisfaction.

[0006] According to one aspect of the present application, a computer vision-based automatic gate control system is provided, comprising:

[0007] The gate entrance image acquisition module is used to obtain the vehicle image at the gate entrance collected by the visual sensor;

[0008] A gate entrance data extraction module is used to extract a gate entrance vehicle appearance enhancement feature vector and a gate entrance vehicle license plate grayscale feature vector from the gate entrance vehicle image collected by the visual sensor;

[0009] The authorized vehicle judgment module is used to judge whether the vehicle at the gate entrance is an authorized vehicle based on the appearance enhancement feature vector of the vehicle at the gate entrance and the grayscale feature vector of the license plate of the vehicle at the gate entrance.

[0010] According to another aspect of the present application, a computer vision-based automatic gate control method is provided, comprising:

[0011] Acquire vehicle images at the gate entrance collected by the visual sensor;

[0012] Extracting a gate entrance vehicle appearance enhancement feature vector and a gate entrance vehicle license plate grayscale feature vector from the gate entrance vehicle image collected by the visual sensor;

[0013] Based on the appearance enhancement feature vector of the vehicle at the gate entrance and the grayscale feature vector of the license plate of the vehicle at the gate entrance, it is determined whether the vehicle at the gate entrance is an authorized vehicle.

[0014] Compared with the existing technology, the present application provides a computer vision-based barrier gate automatic control system and method, which first obtains the vehicle image at the barrier gate entrance collected by the visual sensor, then uses deep learning technology to perform feature extraction and association analysis, and finally obtains the classification result through the classifier to determine whether the vehicle at the barrier gate entrance is an authorized vehicle, thereby sending a corresponding control signal to the barrier gate, thereby reducing the need for manual operation and inspection, improving traffic efficiency, avoiding congestion, and enhancing user experience and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 Schematic diagram of a computer vision-based barrier gate automatic control system according to an embodiment of the present application.

[0017] Figure 2 Schematic diagram of a block diagram of a barrier gate entrance data extraction module in a barrier gate automatic control system based on computer vision according to an embodiment of the present application.

[0018] Figure 3 Schematic diagram of a block diagram of a vehicle appearance feature extraction unit at a barrier entrance in a barrier automatic control system based on computer vision according to an embodiment of the present application.

[0019] Figure 4 Schematic diagram of a block diagram of an authorized vehicle judgment module in a computer vision-based barrier gate automatic control system according to an embodiment of the present application.

[0020] Figure 5 Flowchart of a barrier gate automatic control method based on computer vision according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] Various exemplary embodiments, features, and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0022] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0023] In addition, numerous specific details are provided in the following detailed description to better illustrate the present application. Those skilled in the art will appreciate that the present application can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present application.

[0024] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. Throughout the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0025] Figure 1 FIG is a block diagram of a computer vision-based automatic gate control system according to an embodiment of the present application. Figure 1 As shown, the computer vision-based barrier gate automatic control system 100 according to an embodiment of the present application includes: a barrier gate entrance image acquisition module 110, used to acquire the barrier gate entrance vehicle image collected by the visual sensor; a barrier gate entrance data extraction module 120, used to extract the barrier gate entrance vehicle appearance enhancement feature vector and the barrier gate entrance vehicle license plate grayscale feature vector from the barrier gate entrance vehicle image collected by the visual sensor; an authorized vehicle judgment module 130, used to judge whether the barrier gate entrance vehicle is an authorized vehicle based on the barrier gate entrance vehicle appearance enhancement feature vector and the barrier gate entrance vehicle license plate grayscale feature vector.

[0026] In the aforementioned computer vision-based barrier gate automatic control system 100, the barrier gate entrance image acquisition module 110 is used to acquire images of vehicles at the barrier gate entrance captured by a visual sensor. It should be understood that barrier gates are common automated equipment, widely used in parking lots, residential entrances, industrial park gates, and other locations to control vehicle access. Their primary function is to block or release vehicles by raising or lowering control levers, ensuring that only authorized vehicles can pass. With technological advancements, parking management systems are becoming increasingly intelligent, with access control systems playing a particularly prominent role. Currently, many parking management systems still rely on manual personnel to identify vehicles and operate the barrier gate. However, this manual method is inefficient and prone to errors, potentially impacting user experience and satisfaction. Therefore, in the technical solution of this application, by acquiring images of vehicles at the barrier gate entrance captured by a visual sensor and combining them with deep learning technology, it is determined whether the vehicle at the barrier gate entrance is authorized and then sends corresponding control signals to the barrier gate. This automated process reduces reliance on manual operation and inspection, thereby improving traffic efficiency, reducing congestion, and enhancing user experience and satisfaction.

[0027] Specifically, vision sensors capture images of vehicles at the gate entrance, enabling automated vehicle recognition and analyzing features such as license plate numbers, appearance, and color. By analyzing and applying this image data, management strategies and system functionality can be further optimized, providing a more comprehensive and intelligent solution for parking management.

[0028] In the aforementioned computer vision-based barrier gate automatic control system 100, the barrier gate entrance data extraction module 120 is used to extract the appearance enhancement feature vector and the license plate grayscale feature vector of the barrier gate entrance vehicle from the barrier gate entrance vehicle image captured by the vision sensor. This is crucial for improving recognition accuracy, supporting multiple recognition scenarios, enhancing system intelligence, supporting data analysis and optimization, and enhancing the user experience, thereby achieving more accurate and efficient vehicle identification and management.

[0029] Figure 2 FIG. 1 is a block diagram of a gate entrance data extraction module in a gate automatic control system based on computer vision according to an embodiment of the present application. Figure 2 As shown, in a specific embodiment of the present application, the barrier entrance data extraction module 120 includes: a barrier entrance vehicle appearance feature extraction unit 121, which is used to extract vehicle appearance features from the barrier entrance vehicle image collected by the visual sensor to obtain the barrier entrance vehicle appearance enhancement feature vector; a barrier entrance vehicle license plate feature extraction unit 122, which is used to extract license plate features from the barrier entrance vehicle image collected by the visual sensor to obtain the barrier entrance vehicle license plate grayscale feature vector.

[0030] It should be understood that vehicle appearance feature extraction can capture information such as the vehicle's color, shape, brand logo, and body outline, which are all important features for identifying vehicles. By accurately encoding these details, the appearance-enhanced feature vector can significantly improve the system's recognition accuracy in complex environments. For example, when a license plate is difficult to identify due to damage or poor lighting conditions, the appearance features can still provide effective information to help identify the vehicle. The extracted appearance-enhanced feature vectors are not only used for vehicle identification, but can also support more complex functions such as vehicle classification (such as sedans, SUVs, trucks), behavioral analysis (such as parking location analysis), etc. Through deep learning algorithms, these feature vectors can perform in-depth pattern recognition, giving the system more intelligent functions to meet various management needs.

[0031] Furthermore, license plate feature extraction is performed on vehicle images captured by vision sensors at the gate entrance to achieve efficient and accurate vehicle identification and management. License plate recognition is a core function in vehicle management systems. License plate grayscale feature vector extraction involves converting license plate images into grayscale values, thereby capturing the subtle features of the characters and numbers on the license plate. These grayscale features effectively distinguish different parts of the license plate, such as the shape and arrangement of the letters and numbers, as well as the overall layout of the license plate. This refined feature extraction significantly improves license plate recognition accuracy, especially under conditions with limited license plate clarity or adverse environmental conditions. License plate grayscale feature vector extraction can address the impact of varying lighting, weather, and license plate damage on image quality. Grayscale feature extraction eliminates color information and focuses on shape and structural features, enabling the system to maintain high recognition performance in a variety of environments. Even when the license plate is reflected by light or covered in dirt, the system can still detect the characters on the license plate through grayscale feature extraction, thereby improving recognition robustness.

[0032] Figure 3 FIG. 1 is a block diagram of a vehicle appearance feature extraction unit at a gate entrance in a gate automatic control system based on computer vision according to an embodiment of the present application. Figure 3 As shown, in a specific embodiment of the present application, the vehicle appearance feature extraction unit 121 at the gate entrance includes: a vehicle appearance feature preprocessing subunit 1211 at the gate entrance, which is used to perform feature preprocessing on the vehicle image at the gate entrance collected by the visual sensor to obtain a Canny edge detection map of the vehicle appearance enhanced at the gate entrance; and a vehicle appearance feature encoding subunit 1212 at the gate entrance, which is used to perform feature encoding on the vehicle appearance enhanced Canny edge detection map at the gate entrance to obtain a feature vector of the vehicle appearance enhanced at the gate entrance.

[0033] It should be understood that vehicle images may contain factors such as lighting changes, shadows, and damaged license plates, which may interfere with vehicle recognition. Canny edge detection effectively reduces the impact of these interfering factors on recognition by extracting edge features. Edge detection can provide a more stable and consistent feature representation, allowing the recognition algorithm to maintain high robustness and stability when facing complex environmental conditions, thereby improving the overall performance of the system. Specifically, the Canny edge detection algorithm is a classic image processing technique used to identify edge features in images. By applying Canny edge detection to vehicle images, the vehicle outline and other important edge information can be effectively extracted. This enhanced edge information is crucial for identifying the vehicle's appearance characteristics, as the vehicle's shape and edge features are key visual information in various recognition tasks. By highlighting the vehicle's edges, the system can more clearly capture the vehicle's geometric structure, thereby improving the accuracy of feature extraction.

[0034] Furthermore, the original edge detection image is still a two-dimensional image data, and direct analysis of it can be complex. Feature encoding can convert these edge features into one-dimensional feature vectors. This encoding method not only simplifies the data structure, but also digitizes the edge detection image to express important information in a structured form. During the encoding process, the system can extract key information such as the vehicle's geometric features and edge distribution, and represent this information in the form of feature vectors. This method can enhance the expressive power of features, allowing the recognition algorithm to more accurately capture and utilize the vehicle's appearance features, thereby improving the performance of the recognition system.

[0035] In a specific embodiment of the present application, the vehicle appearance feature preprocessing subunit 1211 at the gate entrance includes: passing the vehicle image at the gate entrance collected by the visual sensor through a vehicle appearance image pixel intensifier at the gate entrance to obtain a pixel-enhanced image of the vehicle appearance at the gate entrance; and performing Canny edge detection on the pixel-enhanced image of the vehicle appearance at the gate entrance to obtain the enhanced Canny edge detection image of the vehicle appearance at the gate entrance.

[0036] It should be understood that the original vehicle image at the gate entrance may be affected by factors such as insufficient lighting, low contrast, and noise interference, resulting in unclear image details. The image pixel enhancer can significantly improve the visual effect of the image by adjusting the brightness, contrast, and clarity of the image, making the details of the vehicle more obvious and easier to identify. Among them, the pixel enhancer can make these important visual information more prominent by optimizing key areas in the image, such as the vehicle outline, license plate, and other identification features. This enhancement makes the edges and details more distinct, helping the recognition system to better extract and identify the vehicle's appearance features. Specifically, the vehicle image at the gate entrance captured by the visual sensor is input into the encoder of the vehicle appearance image pixel enhancer of the gate entrance, wherein the encoder uses a convolution layer to explicitly spatially encode the vehicle image at the gate entrance captured by the visual sensor to obtain image features; and the image features are input into the decoder of the vehicle appearance image pixel enhancer of the gate entrance, wherein the decoder uses a deconvolution layer to deconvolve the image features to obtain the pixel-enhanced image of the vehicle appearance at the gate entrance.

[0037] Furthermore, while pixel boosters can improve overall image quality, some noise or fine details may still be present. The Canny edge detection algorithm uses multi-stage processing (such as Gaussian filtering, gradient calculation, and non-maximum suppression) to effectively suppress the effects of noise and accurately extract edge features. This helps ensure more stable and reliable detection results, reducing errors caused by noise or image artifacts.

[0038] In a specific embodiment of the present application, the barrier entrance vehicle appearance feature encoding subunit 1212 includes: passing the barrier entrance vehicle appearance enhancement Canny edge detection map through a barrier entrance vehicle appearance enhancement Canny edge detection feature encoder based on a spatial attention mechanism to obtain a barrier entrance vehicle appearance enhancement feature map; pooling the barrier entrance vehicle appearance enhancement feature map to obtain the barrier entrance vehicle appearance enhancement feature vector.

[0039] It's understandable that while the edge information extracted from the Canny edge detection image provides essential features of the vehicle's outline and structure, it may still contain a significant amount of irrelevant or minor details. A feature encoder based on the spatial attention mechanism automatically identifies and enhances important feature areas in the image, focusing attention on key areas such as the vehicle's outline and license plate location, while reducing interference from background noise. This feature enhancement helps improve vehicle recognition and localization accuracy. The spatial attention mechanism dynamically adjusts the weights of various image regions, weighting key information to generate a more accurate and rich feature representation. This mechanism can better capture important details in the image, allowing the feature map to more clearly reflect the vehicle's actual appearance, improving the feature map's expressiveness and supporting more complex recognition and classification tasks. Specifically, the convolution coding part of the vehicle appearance enhancement Canny edge detection feature encoder for the barrier entrance based on the spatial attention mechanism is used to perform deep convolution coding on the vehicle appearance enhancement Canny edge detection map at the barrier entrance to obtain an initial convolution feature map; the initial convolution feature map is input into the spatial attention part of the vehicle appearance enhancement Canny edge detection feature encoder for the barrier entrance based on the spatial attention mechanism to obtain a spatial attention map; the spatial attention map is passed through a Softmax activation function to obtain a spatial attention feature map; and the positional point multiplication of the spatial attention feature map and the initial convolution feature map is calculated to obtain the vehicle appearance enhancement feature map at the barrier entrance. More specifically, the initial convolutional feature map is input into the spatial attention part of the vehicle appearance enhancement Canny edge detection feature encoder at the gate entrance based on the spatial attention mechanism to obtain a spatial attention map, including: performing average pooling and maximum pooling along the channel dimension on the initial convolutional feature map to obtain an average feature matrix and a maximum feature matrix; cascading and channel-adjusting the average feature matrix and the maximum feature matrix to obtain a channel feature matrix; and using the convolution layer of the spatial attention feature map to perform convolution encoding on the channel feature matrix to obtain a spatial attention map.

[0040] Furthermore, feature maps often contain high-dimensional data, representing feature information at multiple locations within an image. Pooling these feature maps can compress their spatial dimensions, reducing the dimensionality of the data. Pooling divides the image into small blocks and applies aggregation operations (such as maximum or average) to each block, converting larger feature maps into smaller feature vectors. This not only reduces the computational workload for subsequent processing but also alleviates storage requirements. Pooling helps extract and preserve key information in feature maps. Max pooling selects the maximum value within each pooled region, while average pooling calculates the average value within each pooled region. These operations extract the most significant features from local regions, reducing the impact of local detail variations, thereby removing unnecessary details while preserving key information. By pooling high-dimensional feature maps into low-dimensional feature vectors, computational complexity is significantly reduced. This simplification makes subsequent processing and analysis more efficient. Pooling can improve system processing speed and response time, particularly for large data processing or real-time applications. For example, in vehicle recognition or classification tasks, processing the pooled feature vector is faster and more efficient than processing the original feature map.

[0041] In a specific embodiment of the present application, the vehicle license plate feature extraction unit 122 at the barrier entrance includes: performing grayscale processing on the vehicle image at the barrier entrance collected by the visual sensor to obtain a grayscale image of the vehicle license plate at the barrier entrance; passing the grayscale image of the vehicle license plate at the barrier entrance through a vehicle license plate area target detection model to obtain a region of interest of the vehicle license plate at the barrier entrance; passing the region of interest of the vehicle license plate at the barrier entrance through a vehicle license plate grayscale feature of interest encoder based on a convolutional neural network to obtain a grayscale feature vector of the vehicle license plate at the barrier entrance.

[0042] It should be understood that raw vehicle images are usually in color, containing three color channels: red, green, and blue. Each channel requires a large amount of data to be processed, which makes image processing tasks complex and consumes a lot of computing resources. By converting the image to grayscale, all color information can be compressed into a single channel of data, which greatly simplifies the complexity of the image data. Grayscale images only retain brightness information and remove color information, making subsequent image processing tasks more direct and efficient. In this way, converting the image to grayscale can highlight the edges and details of the license plate characters, reduce the interference of color on the recognition process, and improve the accuracy of license plate recognition. Grayscale images can more clearly show the outlines and textures of the characters, which facilitates character extraction and recognition.

[0043] Furthermore, in vehicle images, the license plate typically occupies a relatively small, specific location. The primary task of the vehicle license plate region object detection model is to identify and locate the exact location of the license plate within the overall image. This process analyzes the image to identify the area containing the license plate and marks it as a region of interest (ROI). The license plate region object detection model can filter out the license plate portion from the entire vehicle image, avoiding the complex process of character recognition on the entire image. By extracting the license plate region from the image, the model only needs to process this specific area, significantly reducing computational effort and processing time. This targeted processing makes license plate recognition more efficient, particularly when processing large numbers of vehicle images, significantly improving the overall processing speed of the system. Specifically, the target anchor layer of the vehicle license plate region object detection model is used to slide the anchor frame B over the grayscale image of the vehicle license plate at the gate entrance to frame the vehicle license plate region of interest within the grayscale image of the vehicle license plate at the gate entrance, thereby obtaining the vehicle license plate region of interest at the gate entrance. Accordingly, in a specific example of the present application, the vehicle license plate region target detection model is Fast R-CNN, Faster R-CNN, or RetinaNet. More specifically, the vehicle license plate region target detection model is used to process the grayscale image of the vehicle license plate at the gate entrance using the following target detection formula to obtain the vehicle license plate region of interest at the gate entrance;

[0044] Among them, the target detection formula is:

[0045] ROI=(cls(ψ det ,B),Rear(ψ det ,B)

[0046] Among them, ψ det is the grayscale image of the vehicle license plate at the gate entrance, B is the anchor frame, ROI is the region of interest of the vehicle license plate at the gate entrance, cls(ψ det ,B) represents classification, Regr(ψ det ,B) indicates regression.

[0047] Furthermore, the license plate area image is processed by a convolutional neural network, automatically extracting high-level feature information. Through multiple convolutional, pooling, and activation layers, a convolutional neural network captures important image features, such as edges, texture, and shape, which are crucial for license plate character recognition. Through these network layers, a CNN extracts effective feature representations from complex image data, generating a grayscale feature vector for the license plate that highly summarizes the core information. The convolutional neural network converts the two-dimensional image data of the license plate region of interest into a high-dimensional feature vector. This feature vector is a condensed representation of the characteristics of the license plate area, providing a digital representation of the key information in the image. The length and dimension of the feature vector can vary depending on the design of the network architecture. It compresses the information of the license plate area into a fixed-length vector, facilitating subsequent processing and comparison. This approach not only reduces data redundancy but also improves computational efficiency. Specifically, each layer of the vehicle license plate grayscale feature of interest encoder at the barrier entrance based on the convolutional neural network is used to perform convolution processing, mean pooling processing based on the local feature matrix, and nonlinear activation processing on the input data in the forward pass of the layer, so that the last layer of the vehicle license plate grayscale feature of interest encoder at the barrier entrance based on the convolutional neural network outputs the vehicle license plate grayscale feature vector at the barrier entrance, wherein the input of the vehicle license plate grayscale feature of interest encoder at the barrier entrance based on the convolutional neural network is the vehicle license plate region of interest at the barrier entrance.

[0048] In the aforementioned computer vision-based barrier gate automatic control system 100, the authorized vehicle judgment module 130 is used to determine whether the vehicle entering the barrier gate is an authorized vehicle based on the vehicle appearance enhancement feature vector and the license plate grayscale feature vector. It should be understood that the vehicle appearance enhancement feature vector and the license plate grayscale feature vector provide two important dimensions of vehicle identity. The appearance enhancement feature vector helps the system identify and distinguish different vehicles by processing visual features such as the vehicle's shape, color, and size. The license plate grayscale feature vector, on the other hand, focuses on the specific information of the license plate characters, which is crucial for verifying the uniqueness and legitimacy of the vehicle. By combining these two features, the system can more comprehensively identify vehicles and reduce the possibility of misidentification.

[0049] Figure 4 FIG. 1 is a block diagram of an authorized vehicle determination module in a computer vision-based automatic gate control system according to an embodiment of the present application. Figure 4As shown, in a specific embodiment of the present application, the authorized vehicle judgment module 130 includes: a gate entrance vehicle feature fusion unit 131, which is used to perform back propagation error correction based on synchronous microscopic dimensions on the gate entrance vehicle appearance enhancement feature vector and the gate entrance vehicle license plate grayscale feature vector to obtain an authorized vehicle judgment classification feature vector; a gate entrance vehicle authorization judgment classification unit 132, which is used to pass the authorized vehicle judgment classification feature vector through a classifier to obtain a classification result, and the classification result is used to determine whether the gate entrance vehicle is an authorized vehicle.

[0050] It's easy to understand that by integrating the vehicle appearance enhancement feature vectors at the gate entrance with the license plate grayscale feature vectors, the vehicle identification and authorization process can be intelligently implemented. This integration simplifies subsequent data processing, reduces manual intervention, and improves management efficiency. The system can quickly generate a classification feature vector for authorized vehicles and automatically verify vehicle identity, improving overall operational efficiency and accuracy.

[0051] Specifically, in the technical solution of this application, the vehicle appearance image captures the vehicle's overall morphology and texture information through pixel enhancement, edge detection, and feature encoding. These features are typically based on the image's spatial structure and edge information and have a strong descriptive power for the vehicle's appearance and state. In contrast, the license plate image undergoes grayscale processing, object detection, and feature encoding, focusing on the regional features of the license plate and the license plate number. These features primarily focus on the accuracy of details, such as the edges and shape of the license plate characters. The two features focus on different levels of detail and information dimensions during processing, resulting in inconsistent dimensionality and fine-grained representation in the feature space, thus causing a shift in the fine-grained dimensionality of the features. Furthermore, due to the differences in feature dimensions and representation details between the vehicle appearance enhancement feature vector and the license plate grayscale feature vector, local structural information (such as the precise shape of the license plate characters and the overall texture of the vehicle appearance) may be diluted or lost during the simple fusion process. Specifically, the global features of the vehicle appearance may not effectively complement the license plate details, and the license plate details may also fail to enhance the overall vehicle recognition effect. This information mismatch and structural destruction means that the final feature vector may not accurately reflect all vehicle features, affecting the judgment performance of the classifier. Therefore, in the technical solution of the present application, the authorized vehicle judgment classification feature vector is obtained by performing back propagation error correction based on the synchronous microscopic dimension on the vehicle appearance enhancement feature vector at the gate entrance and the vehicle license plate grayscale feature vector at the gate entrance.

[0052] Among them, the vehicle appearance enhancement feature vector at the gate entrance and the vehicle license plate grayscale feature vector at the gate entrance are subjected to back propagation error correction based on synchronous microscopic dimensions to obtain the authorized vehicle judgment classification feature vector, including: calculating the position difference between the vehicle appearance enhancement feature vector at the gate entrance and the vehicle license plate grayscale feature vector at the gate entrance to obtain the back propagation error correction differential feature vector; calculating the position point multiplication between the vehicle appearance enhancement feature vector at the gate entrance and the vehicle license plate grayscale feature vector at the gate entrance to obtain the back propagation error correction position point multiplication feature vector; calculating the position addition between the vehicle appearance enhancement feature vector at the gate entrance and the vehicle license plate grayscale feature vector at the gate entrance to obtain the back propagation error correction position addition feature vector; and adding the back propagation error correction differential feature vector and the back propagation error correction position point multiplication feature vector. The vector and the back propagation error correction position-added feature vector are cascaded and input into a one-dimensional convolution layer and maximum pooling is performed to obtain a back propagation error correction convolution coding pooling feature vector; the square root of the two norm of the back propagation error correction convolution coding pooling feature vector is weighted and added to the one norm of the back propagation error correction convolution coding pooling feature vector to obtain a back propagation error correction factor; the back propagation error correction position-based dot product feature vector and the square root of the length of the vehicle appearance enhancement feature vector at the gate entrance are added positionally to obtain a back propagation error correction intermediate vector; the back propagation error correction factor is used as the first weighting coefficient and the second weighted hyperparameter is used as the second weighting coefficient to calculate the position-weighted sum of the back propagation error correction position-added feature vector and the back propagation error correction intermediate vector to obtain the authorized vehicle judgment classification feature vector.

[0053] Among them, the appearance enhancement feature vector of the vehicle at the gate entrance and the grayscale feature vector of the vehicle license plate at the gate entrance are corrected by back propagation error based on the synchronous microscopic dimension to obtain the authorized vehicle judgment classification feature vector, including:

[0054]

[0055]

[0056] Among them, V1 represents the appearance enhancement feature vector of the vehicle at the gate entrance, v2 represents the grayscale feature vector of the vehicle license plate at the gate entrance, represents positional subtraction, ⊙ represents positional multiplication, Indicates addition by position, conv1D indicates a one-dimensional convolutional layer, MaxPool indicates maximum pooling, V μrepresents the back-propagation error correction convolutional coding pooling feature vector, ||·||1 represents the first norm, ||·||2 represents the second norm, [·] represents the cascade, α represents the first weighted hyperparameter, τ represents the back-propagation error correction factor, L represents the length of the vehicle appearance enhancement feature vector at the gate entrance, β represents the second weighted hyperparameter, V f Represents the authorized vehicle judgment classification feature vector.

[0057] In the technical solution of the present application, in the process of fusing the vehicle appearance enhancement feature vector at the gate entrance and the vehicle license plate grayscale feature vector at the gate entrance, the fine-grained dimension offset between the vehicle appearance enhancement feature vector at the gate entrance and the vehicle license plate grayscale feature vector at the gate entrance is taken into account, which may cause the fused authorized vehicle judgment classification feature vector to have local structural collapse. Based on this, in the technical solution of the present application, the vehicle appearance enhancement feature vector at the gate entrance and the vehicle license plate grayscale feature vector at the gate entrance are subjected to back-propagation error correction based on synchronized microscopic dimensions.

[0058] Specifically, based on the restricted expression of the foreground-background structure distinction of the high-dimensional manifold of the vehicle appearance enhancement feature vector at the barrier entrance and the vehicle license plate grayscale feature vector by the amplitude of the semantic difference vector between the vehicle appearance enhancement feature vector at the barrier entrance and the vehicle license plate grayscale feature vector at the barrier entrance, the key feature fine-grained correspondence between the vehicle appearance enhancement feature vector at the barrier entrance and the vehicle license plate grayscale feature vector at the barrier entrance is modeled, and then the active fine-grained correspondence adjustment propagation is performed through the back-propagation displacement compensation information between the corresponding eigenvalues ​​of the vehicle appearance enhancement feature vector at the barrier entrance and the vehicle license plate grayscale feature vector at the barrier entrance, so as to avoid the imbalance at the feature vector level of the vehicle appearance enhancement feature vector at the barrier entrance and the vehicle license plate grayscale feature vector at the barrier entrance in a focusing manner, thereby synchronizing the micro-dimensions of the two feature vectors, and ensuring that the information between the feature vectors can be effectively aligned and combined during the fusion process, thereby avoiding the abnormal accumulation of local structures.

[0059] Furthermore, the authorized vehicle classification feature vector is fed into a classifier, which comprehensively considers multiple feature dimensions (such as vehicle appearance and license plate information) and analyzes the combination of these features to determine the vehicle's authorization status. The classifier uses complex algorithms to process these features, capturing complex patterns and relationships, improving the comprehensiveness of its judgment, thereby enhancing the efficiency and accuracy of vehicle management at the gate entrance and ensuring the stability and adaptability of the management system.

[0060] In another embodiment of the present application, a smart control panel is provided. Specifically, the tidal light function within the smart control panel includes a tidal bidirectional indicator light that can automatically adjust its color based on the license plate recognition status. Specifically, when a license plate is recognized or payment is being made for entry or exit, the light turns green, and the reverse indicator turns red. If no license plate is recognized, the light returns to green. More specifically, the smart control panel features an anti-following vehicle function. This function automatically closes the gate after a timeout (default is 0 seconds, but can be configured to 15 seconds via software). It also intelligently determines the following vehicle status and automatically memorizes the number of gate openings, ensuring normal vehicle passage, effectively resolving various following vehicle issues. When the smart control panel receives a remote gate opening signal, it initiates gate lift and pushes the gate opening record to the cloud backend, triggering an IMS alert. When a queue mode signal is received, the gate bar opens immediately and remains open. Furthermore, the smart control panel monitors gate signals, updates the current gate bar open or closed status in real time, and supports real-time temperature monitoring and reporting. The intelligent control panel supports special needs functions. In the event of a sudden power outage or other serious fault, the device can intelligently identify and automatically open the barrier. Furthermore, the intelligent control panel can remotely resolve power outages and restarts in the event of an on-site identification machine or radar equipment failure. Furthermore, after a vehicle entry / exit barrier is raised, if the license plate has not entered or exited the lot, the intelligent control panel automatically processes the invalid order. It also supports timed barrier status monitoring, providing voice notifications and automatically lowering the barrier if a timeout occurs.

[0061] In summary, the embodiment of the present application first obtains the vehicle image at the gate entrance collected by the visual sensor, then uses deep learning technology to perform feature extraction and association analysis, and finally obtains the classification result through the classifier to determine whether the vehicle at the gate entrance is an authorized vehicle, thereby sending a corresponding control signal to the gate, thereby reducing the need for manual operation and inspection, improving traffic efficiency, avoiding congestion, and enhancing user experience and satisfaction.

[0062] As described above, the computer vision-based automatic barrier gate control system 100 according to the embodiments of the present application can be implemented in various terminal devices. In one example, the computer vision-based automatic barrier gate control system 100 can be integrated into the terminal device as a software module and / or hardware module. For example, the computer vision-based automatic barrier gate control system 100 can be a software module in the operating system of the terminal device, or can be an application developed for the terminal device; of course, the computer vision-based automatic barrier gate control system 100 can also be one of the many hardware modules of the terminal device.

[0063] Alternatively, in another example, the computer vision-based barrier gate automatic control system 100 and the terminal device may also be separate devices, and the computer vision-based barrier gate automatic control system 100 may be connected to the terminal device through a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0064] Figure 5 Flowchart of the automatic gate control method based on computer vision according to an embodiment of the present application. Figure 5 As shown, according to the computer vision-based automatic barrier gate control method of an embodiment of the present application, it includes: S110, acquiring a barrier gate entrance vehicle image captured by a visual sensor; extracting a barrier gate entrance vehicle appearance enhancement feature vector and a barrier gate entrance vehicle license plate grayscale feature vector from the barrier gate entrance vehicle image captured by the visual sensor; based on the barrier gate entrance vehicle appearance enhancement feature vector and the barrier gate entrance vehicle license plate grayscale feature vector, determining whether the barrier gate entrance vehicle is an authorized vehicle.

[0065] Here, those skilled in the art will appreciate that the specific operations of each step in the above-mentioned automatic gate control method based on computer vision have been described in the above reference. Figures 1 to 4 The description of the computer vision-based barrier gate automatic control system has been introduced in detail, and therefore, its repeated description will be omitted.

[0066] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0067] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0068] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0069] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0070] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0071] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Second-order terms are used to indicate names and do not imply any particular order.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit of the technical solutions of the present invention.

Claims

1. A gate automatic control system based on computer vision, characterized in that: include: The gate entrance image acquisition module is used to obtain the vehicle image at the gate entrance collected by the visual sensor; A gate entrance data extraction module is used to extract a gate entrance vehicle appearance enhancement feature vector and a gate entrance vehicle license plate grayscale feature vector from the gate entrance vehicle image collected by the visual sensor; An authorized vehicle judgment module is used to judge whether the vehicle entering the gate is an authorized vehicle based on the appearance enhancement feature vector of the vehicle at the gate entrance and the grayscale feature vector of the license plate of the vehicle at the gate entrance; The gate entrance data extraction module includes: A vehicle appearance feature extraction unit at a gate entrance is configured to extract vehicle appearance features from the vehicle image at the gate entrance captured by the visual sensor to obtain an enhanced feature vector of the vehicle appearance at the gate entrance; a vehicle license plate feature extraction unit at a gate entrance, configured to extract license plate features from the vehicle image at the gate entrance captured by the visual sensor to obtain a grayscale feature vector of the vehicle license plate at the gate entrance; The authorized vehicle determination module includes: The gate entrance vehicle feature fusion unit is used to perform back propagation error correction based on the synchronous micro-dimension on the gate entrance vehicle appearance enhancement feature vector and the gate entrance vehicle license plate grayscale feature vector to obtain the authorized vehicle judgment classification feature vector; A gate entrance vehicle authorization judgment classification unit is used to pass the authorized vehicle judgment classification feature vector through a classifier to obtain a classification result, and the classification result is used to determine whether the gate entrance vehicle is an authorized vehicle; The gate entrance vehicle feature fusion unit includes: Calculating the position difference between the vehicle appearance enhancement feature vector at the gate entrance and the vehicle license plate grayscale feature vector at the gate entrance to obtain a back-propagation error correction differential feature vector; Calculating the position-based point multiplication between the vehicle appearance enhancement feature vector at the gate entrance and the vehicle license plate grayscale feature vector at the gate entrance to obtain a back-propagation error correction position-based point multiplication feature vector; Calculating the position-based addition of the vehicle appearance enhancement feature vector at the gate entrance and the vehicle license plate grayscale feature vector at the gate entrance to obtain a back propagation error correction position-based addition feature vector; The back-propagation error correction differential feature vector, the back-propagation error correction position point product feature vector and the back-propagation error correction position addition feature vector are concatenated and input into a one-dimensional convolution layer and subjected to maximum pooling to obtain a back-propagation error correction convolutional coding pooling feature vector; Weighting the square root of the two-norm of the back-propagation error correction convolutional coding pooling feature vector and adding the square root of the two-norm to the one-norm of the back-propagation error correction convolutional coding pooling feature vector to obtain a back-propagation error correction factor; The back propagation error correction is performed by adding the position-based dot product feature vector of the back propagation error correction and the square root of the length of the vehicle appearance enhancement feature vector at the gate entrance to obtain a back propagation error correction intermediate vector; Using the back propagation error correction factor as the first weighting coefficient and the second weighted hyperparameter as the second weighting coefficient, the position-weighted sum of the back propagation error correction position-added feature vector and the back propagation error correction intermediate vector is calculated to obtain the authorized vehicle judgment classification feature vector.

2. The automatic gate control system based on computer vision according to claim 1 is characterized in that: The vehicle appearance feature extraction unit at the gate entrance includes: a vehicle appearance feature preprocessing subunit at a gate entrance, configured to perform feature preprocessing on the vehicle image at the gate entrance captured by the visual sensor to obtain an enhanced Canny edge detection image of the vehicle appearance at the gate entrance; The gate entrance vehicle appearance feature encoding subunit is used to perform feature encoding on the gate entrance vehicle appearance enhancement Canny edge detection image to obtain the gate entrance vehicle appearance enhancement feature vector.

3. The automatic gate control system based on computer vision according to claim 2 is characterized in that: The gate entrance vehicle appearance feature preprocessing subunit includes: Passing the gate entrance vehicle image collected by the visual sensor through the gate entrance vehicle appearance image pixel intensifier to obtain a gate entrance vehicle appearance pixel enhanced image; Canny edge detection is performed on the pixel-enhanced image of the vehicle appearance at the gate entrance to obtain an enhanced Canny edge detection image of the vehicle appearance at the gate entrance.

4. The automatic gate control system based on computer vision according to claim 3 is characterized in that: The gate entrance vehicle appearance feature coding subunit includes: The gate entrance vehicle appearance enhancement Canny edge detection image is passed through the gate entrance vehicle appearance enhancement Canny edge detection feature encoder based on the spatial attention mechanism to obtain a gate entrance vehicle appearance enhancement feature image; The vehicle appearance enhancement feature map at the gate entrance is pooled to obtain the vehicle appearance enhancement feature vector at the gate entrance.

5. The automatic gate control system based on computer vision according to claim 4 is characterized in that: The vehicle license plate feature extraction unit at the gate entrance includes: Performing grayscale processing on the vehicle image at the gate entrance collected by the visual sensor to obtain a grayscale image of the vehicle license plate at the gate entrance; The grayscale image of the vehicle license plate at the gate entrance is passed through a vehicle license plate area target detection model to obtain a region of interest of the vehicle license plate at the gate entrance; The vehicle license plate region of interest at the gate entrance is passed through a vehicle license plate grayscale feature of interest encoder based on a convolutional neural network to obtain a vehicle license plate grayscale feature vector at the gate entrance.

6. A computer vision-based automatic gate control method, the method is implemented based on the system according to claim 1, characterized in that: include: Acquire vehicle images at the gate entrance collected by the visual sensor; Extracting a gate entrance vehicle appearance enhancement feature vector and a gate entrance vehicle license plate grayscale feature vector from the gate entrance vehicle image collected by the visual sensor; Determining whether the vehicle at the gate entrance is an authorized vehicle based on the appearance enhancement feature vector of the vehicle at the gate entrance and the grayscale feature vector of the license plate of the vehicle at the gate entrance; The method of extracting the appearance enhancement feature vector of the vehicle at the gate entrance and the license plate grayscale feature vector of the vehicle at the gate entrance from the vehicle image at the gate entrance collected by the visual sensor includes: Extracting vehicle appearance features from the vehicle image at the gate entrance captured by the visual sensor to obtain an enhanced feature vector of the vehicle appearance at the gate entrance; The license plate feature is extracted from the vehicle image at the gate entrance collected by the visual sensor to obtain a grayscale feature vector of the vehicle license plate at the gate entrance.

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