Road irregular parking identification system and equipment

Abstract

The present invention discloses a road irregular parking recognition system and equipment. The system is based on the following recognition method: collecting and processing images of parking areas to reproduce the approximate portion of parking space lines; extracting parking space lines, removing noise, and morphologically reconstructing to obtain parking space line contours; calculating the proximity of the ground and the parking space contours, extracting pixel coordinates, and obtaining adjusted parking space line contours; searching for and marking the license plate position in the image; clustering analysis of the contours to calculate the approximate directions of each contour portion and plan the location of the parking space; calculating the intersection of the normal vector equation of the license plate to the road surface and the parking space direction equation and determining whether the intersection solution is within a range of values, thereby determining the relative position of the vehicle and the parking space. The present invention utilizes computer vision and image processing algorithms to effectively improve the recognition rate of vehicle parking behavior and the robustness of the algorithm, reduce the amount of calculation, and reduce the cost of manual inspections. It can be used in the field of intelligent transportation.

Description

[0001] This application is a divisional application of the Chinese invention patent application with application number "202410539191.2", application date "2024.04.30", and invention name "Method for identifying irregular parking on roads". Technical Field

[0002] The present invention relates to the field of intelligent transportation technology, specifically to a system and equipment for identifying irregular parking on a road, and more particularly to a method, system, equipment and medium for identifying irregular parking on a road. Background Art

[0003] In recent years, the number of motor vehicles in my country has continued to grow, making cars an indispensable means of transportation for people. Parking has become an increasingly prominent issue. Especially in busy commercial areas, residential communities, and near public transportation hubs, finding a suitable parking space has become a headache for many drivers. Planning for on-street parking can significantly alleviate parking pressure and is gaining increasing popularity. Currently, on-street parking supervision in most Chinese cities is still primarily manual, a method that incurs significant labor costs, is extremely inefficient, and struggles to maximize parking space utilization. Errors are common, especially during peak parking periods. Therefore, the development of efficient parking space and image recognition technology is urgent. However, practical application of parking space and vehicle image recognition technology still faces several challenges. For example, factors such as different vehicle models, lighting conditions, and obstructions can affect image recognition accuracy. Furthermore, with the continuous advancement of technology and the increasing diversity of user needs, further improving recognition accuracy, optimizing user experience, and reducing system costs have become both hot and challenging research areas in this field.

[0004] At present, there are two main methods for managing roadside parking spaces: geomagnetic-based and video-based. The geomagnetic-based parking space detection method has high detection accuracy, but it requires manual installation of geomagnetic detectors, which has high manufacturing costs, and there are problems such as difficulty in endurance and susceptibility to signal interference. The video-based parking space detection method does not require additional equipment to be installed, but video detection is greatly affected by the environment, and the accuracy of parking space detection is easily affected at long distances. Therefore, based on this problem, those skilled in the art have proposed a method for identifying irregular parking on roads, which uses computer vision and image processing algorithms to effectively reduce costs and take into account various influencing factors in the actual state of roadside parking spaces, effectively reducing the computational complexity of determining the parking position analysis of roadside vehicles, and greatly reducing the degree of computing resource requirements. Summary of the Invention

[0005] Technical issues

[0006] To address the aforementioned issues, the present invention aims to provide a system and device for identifying irregular parking on roads. Based on computer vision, this system utilizes image processing algorithms to detect parking behavior, thereby addressing the problem of detecting on-street parking spaces. The accuracy of image recognition in this method is unaffected by factors such as vehicle type, lighting conditions, and obstructions.

[0007] Technical Solution

[0008] In order to achieve the above object, the present invention provides a method for identifying irregular parking on a road, comprising the following steps:

[0009] 1) Read the image and convert the RGB image to HSV space;

[0010] 2) Process the HSV space and reproduce the approximate position of the parking space line;

[0011] 3) For parking space line extraction, remove noise, perform morphological reconstruction, and obtain the parking space line contour;

[0012] 4) Calculate the approximate portion of the ground contour line and the parking space contour line, extract the pixel point coordinates, and obtain the parking space contour line adjusted by the ground contour line;

[0013] 5) Search for the license plate position in the image and mark it in the parking space line contour image in step 4);

[0014] 6) Perform cluster analysis on the contour lines, calculate the intersection of the normal vector equation of the license plate to the road surface and the parking space direction equation, and determine whether the intersection solution is within the value range, thereby determining the relative position of the vehicle and the parking space.

[0015] Furthermore, in step 1), the image reading and conversion of the RGB image into the HSV space specifically includes: collecting an image in the parking area through a camera as an input for detection, and returning the matrix data of the workspace after the image is loaded; and converting the obtained RGB image into the HSV space through a series of mathematical calculations.

[0016] Furthermore, in step 2), the ground and parking space wireframe are generally identified based on the obtained HSV component map, and the approximate positions of the ground and the parking space wireframe are determined, and finally copied into the blank image.

[0017] Furthermore, in step 3), the parking space line extraction uses a "filter" with a set range size to filter out noise points. This filter is applied to all pixels in the image to obtain the coordinates of the noise points. At the locations of the obtained noise point coordinates, all pixels are assigned a value of 0. Morphological reconstruction techniques are used to fill in gaps or missing wireframes. Finally, an approximate portion of the parking space line is reconstructed.

[0018] Furthermore, in step 3), obtaining the parking space line contour includes: calling the Canny edge detection algorithm to accurately detect the image edge through Gaussian blur denoising, calculating gradient amplitude and direction, non-maximum suppression, dual threshold detection and hysteresis threshold.

[0019] Furthermore, the step 4) includes intercepting the ground contour and extracting the ground contour line.

[0020] Furthermore, in step 4), the specific steps for calculating the approximate portions of the ground contour and the parking space contour, extracting pixel coordinates, and obtaining the parking space contour adjusted by the ground contour are as follows: comparing the ground contour coordinates with the parking space contour coordinates, setting a threshold, and traversing all coordinate points of the two. If the pixel coordinate area of the two exceeds the threshold, the two are judged to be disjoint; otherwise, they are judged to be close. The parking space line frame coordinates that are close to the ground contour coordinates are filtered out to obtain the adjusted parking space contour. After filtering, the coordinates of the actual parking space contour are mostly retained, while some larger noise blocks that were not removed during the previous noise removal can be removed in this step.

[0021] Furthermore, the step 5) specifically includes: searching for the license plate position in the image through the "license plate search algorithm", taking a screenshot of its image and pixel coordinates, and marking it in the parking space line contour image of step 4) according to its pixel coordinates.

[0022] Furthermore, step 6) specifically includes: performing cluster analysis on the contour line to obtain the category of each part of the contour line, specifically including: clustering the coordinates of the parking space wireframe using a Gaussian mixture clustering algorithm (GMM algorithm) to distinguish different edges of the parking space wireframe.

[0023] Furthermore, step 6) also includes: solving the slope and intercept coefficient of the edges of the clustered parking space frames according to linear programming, representing the direction of the wireframe by a straight line, and taking the average direction of the wireframe as the parking space direction to obtain the parking space direction equation; the wireframe size of the parking space includes value range information; making a normal vector equation of the license plate to the road surface through the direction of the license plate to the wireframe; solving the equation system consisting of the normal vector equation of the license plate to the road surface and the parking space direction equation; if the equation system has an intersection solution and the intersection solution is within the value range, the vehicle is inside the parking space; otherwise, it is outside the parking space, thereby determining the relative position of the vehicle and the parking space.

[0024] Based on the image shooting detection method, the present invention uses the intersection of the normal vector equation of the license plate to the road surface and the parking space direction equation to determine the relative position of the vehicle and the parking space, effectively reducing the influence of adverse factors such as different vehicle models, lighting conditions, and obstructions, and effectively reducing the computational complexity of determining the parking position analysis of roadside vehicles, and greatly reducing the demand for computing resources. The present invention focuses on the recognition of the relative relationship between parking spaces and vehicles, and is used to determine whether a vehicle stays in a parking space under various abnormal circumstances, thereby providing services for vehicle parking billing, illegal parking, and abnormal state processing.

[0025] In a second aspect, the present invention further provides a system for identifying irregular parking on a road, which determines a parking space and a relative parking position based on the aforementioned irregular parking recognition method.

[0026] In a third aspect, the present invention also provides a computer device comprising a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory so that the computer device performs the steps of implementing the above method.

[0027] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0028] Beneficial effects

[0029] By implementing the above-mentioned road irregular parking identification system provided by the present invention, the following technical effects are achieved:

[0030] (1) Based on the image capture detection method, the method provided by this technical solution uses the intersection of the normal vector equation of the license plate to the road surface and the parking space direction equation to determine the relative position of the vehicle and the parking space, effectively reducing the influence of adverse factors such as different vehicle models, lighting conditions, and obstructions, effectively reducing the computational complexity of roadside vehicle parking position analysis, and significantly reducing the degree of computing resource requirements;

[0031] (2) The method provided by this technical solution can achieve highly efficient parking space detection and retrieval, effectively maximizing the utilization rate of parking space resources;

[0032] (3) The method provided by this technical solution does not require the installation of additional equipment for parking spaces where vehicles are parked on the roadside, thereby reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to make the above-mentioned image recognition algorithm and computer vision algorithm of the present invention more obvious and easy to understand, the following is a brief introduction to the drawings required for use in the specific implementation of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 Represents the workspace variable for loading images;

[0035] Figure 2 Represents a graphics window showing the loaded image;

[0036] Figure 3 Indicates conversion from RGB to HSV;

[0037] Figure 4 Indicates the approximate identification of the parking space wireframe;

[0038] Figure 5 Indicates noise removal;

[0039] Figure 6 Indicates morphological reconstruction and superposition to remove noise;

[0040] Figure 7 Represents the outline of the parking space wireframe extracted by the Canny algorithm;

[0041] Figure 8 It indicates the interception of the ground;

[0042] Figure 9 Represents the extraction of ground contour lines;

[0043] Figure 10 The coordinates of the parking space outline frame close to the ground contour line;

[0044] Figure 11 Represents the contour line of the parking space wireframe coordinates after filtering with the ground contour line coordinates;

[0045] Figure 12 Indicates a screenshot of a license plate;

[0046] Figure 13 Indicates the relative position of the license plate in the parking space wireframe outline;

[0047] Figure 14 Cluster analysis of parking space wireframes;

[0048] Figure 15 Linear program representing the parking space wireframe and license plate location. DETAILED DESCRIPTION

[0049] The present invention will be further described below with reference to the accompanying drawings.

[0050] The present invention discloses a method for identifying irregular parking on a road, comprising the following steps: collecting an image in a parking area by a camera, reading the image and converting the RGB image into an HSV space, identifying the current status of the ground and the parking space according to the obtained HSV component map, determining the approximate location of the ground and the current status of the parking space, and finally replicating them into a blank image; for parking space line extraction, setting "filter points" is used to remove noise points, and using morphological reconstruction to supplement the holes in the wireframe to obtain the parking space line contour line; calculating the close parts of the ground and the parking space contour line, extracting the pixel coordinates, and obtaining the parking space line contour line adjusted by the ground contour line; searching for the license plate position in the image by a "license plate search algorithm", and marking it in the parking space line contour line image; clustering analysis of the contour line, calculating the approximate directions of each part of the contour line, and planning the location of the parking space; and finally judging the relative position of the vehicle and the parking space by judging whether the equation group consisting of the normal vector ray equation and the parking space direction equation has a solution within a value range.

[0051] The method for identifying irregular parking on a road provided by the present invention specifically includes the following steps:

[0052] 1) Read the image and convert the RGB image to HSV space.

[0053] In certain embodiments, algorithm programs developed using Matlab software must be run on the Matlab platform. Alternatively, Matlab programs can be packaged as .exe files and run independently, or loaded and run in other programming languages such as Python through APIs. Loading images into Matlab requires specifying the image format, such as 'jpg', 'png', or 'tif'. However, Matlab can typically automatically determine the correct format based on the file extension. For images containing multiple pages (e.g., multi-frame GIF or TIFF files), the frame to be loaded can also be specified.

[0054] The present invention uses a camera to collect images of the parking area as the input for detection. After the image is loaded, the matrix data of the workspace is returned. For color images, an M×N×3 matrix is returned, where the third dimension is used to store the red, green, and blue (RGB) color channels. Figure 1 As shown, Figure 1 Represents color image matrix data.

[0055] RGB color space: RGB stands for the three primary colors red, green, and blue. An RGB image uses color information from three channels, red, green, and blue, to describe each pixel. The value of each channel is typically represented by an integer between 0 and 255. By adjusting the values of these three channels, a variety of colors can be mixed. The RGB color space is a hardware-oriented color model widely used in computer graphics and image processing.

[0056] HSV color space: HSV stands for Hue, Saturation, and Value. The HSV model describes color based on its intuitive properties, more consistent with human visual perception. Hue represents the basic properties of color, such as red and green; saturation indicates the purity or vividness of a color; and value indicates its brightness. The HSV color space is commonly used for tasks such as image segmentation, color recognition, and color adjustment.

[0057] Compared to RGB images, the HSV color space is more suitable for tasks such as image segmentation, color recognition, and color adjustment. Therefore, the present invention requires converting RGB images into the HSV color space. RGB and HSV are two different color spaces, each describing color in a different way. Converting an RGB image to HSV is the process of converting an image from RGB to HSV.

[0058] Converting an RGB image to the HSV color space requires the following steps: Calculate the maximum, minimum, and difference values in the three RGB channels; Determine the hue, saturation, and brightness in the HSV space based on the relationship between the difference and the maximum / minimum values; Normalize the calculated HSV values to make them conform to the value range of the HSV color space. Through this conversion process, each pixel in the RGB image is mapped to the corresponding position in the HSV color space, thereby realizing the conversion of the RGB image to the HSV space. This conversion helps to extract color features in the image, facilitating subsequent image processing and analysis tasks. Figure 3 As shown, Figure 3 Represents the HSV component map, where Hue, Saturation, and Color Value represent hue, saturation, and color brightness, respectively.

[0059] 2) Process the HSV space and replicate the approximate location of the parking space line.

[0060] according to Figure 3The HSV component graph in the image determines the recognition of different colors of the ground and parking space status. It can be seen that in the recognition of the ground and parking space status, the H (Hue) dimension tends to recognize the ground as completely black, the S (Saturation) dimension is also completely black, and the V (Color Value) dimension has a clear distinction between the ground and the parking space wireframe. In reality, the parking space wireframe must be on the road surface. The ground position is determined based on the H (Hue) and S (Saturation) dimensions, and the approximate location of the parking space status is filtered using the V (Color Value) dimension and reproduced in the blank image, as shown in the following example. Figure 4 shown.

[0061] 3) For parking space line extraction, remove noise, perform morphological reconstruction, and obtain the parking space line contour.

[0062] like Figure 4 As shown in the figure, after obtaining the binary image of the parking space wireframe, there are still some noises in the background and road surface. Since we copied the recognized image to a completely white blank image in the previous step, Figure 4 Most of the space in the image is completely white, meaning it has a binarized value of 0. However, the areas containing parking space wireframes have a binarized value of 1, and the pixels are generally continuous. In contrast, although some of the noise also has a binarized value of 1, the noise is generally smaller, meaning the pixels are discontinuous.

[0063] First, we filter out noise by setting a "filter" within a certain range. Specifically, we apply a circular filter with a radius of 3-20 pixels (the parameter can be set). Within the locations covered by the circular filter, if more than 90% of the pixels are 1 (the parameter can be set), they are considered to be parking space wireframes. If 0% of the pixels are 1, they are considered to be blank. Otherwise, they are considered to be noise. This filter is applied to all pixels in the image to obtain the coordinates of the noise points. For the obtained coordinates of the noise points, all pixels are assigned a value of 0. Figure 5 Represents an image with noise removed.

[0064] After filtering out the noise, there are still some problems of holes and missing wireframes. The present invention uses morphological reconstruction technology to supplement this problem. Morphological reconstruction is an image processing technology based on morphological processing. It involves two images and a structural element: one image is used as a marker and is the starting point of the transformation; the other image is used as a template to constrain the transformation process; and the structural element is used to define connectivity. The marker image (starting point) is repeatedly expanded until the outline of the marker image matches the template image. Morphological reconstruction includes two steps: seed point marking and expansion. The seed point refers to the position of the target we want to reconstruct in the image. These seed points can be selected manually or automatically obtained through image segmentation algorithms. Morphological reconstruction uses the shape and structure information in the image to locate, segment and measure the target. In addition, the opening operation based on morphological reconstruction can accurately restore the shape of the object before corrosion and retain only the image of the target area. Figure 6 Represents the image after morphological reconstruction and superposition to remove noise.

[0065] at this time Figure 6 The wireframe is already relatively complete, so we can perform edge detection on it and extract the outline wireframe. This method uses the classic Canny edge detection algorithm to perform edge detection on the image and extract the outline wireframe, following three main criteria: low error rate, accurate positioning, and clear edge correspondence.

[0066] The specific steps of the Canny edge detection algorithm are:

[0067] 1. Gaussian blur denoising. Edge detection algorithms are usually very sensitive to noise. Therefore, before edge detection, filtering and noise reduction are required. In the Canny algorithm, a Gaussian filter is used to smooth the image to reduce the noise in the image.

[0068] 2. Calculate the gradient magnitude and direction. The core of edge detection is to calculate the gradient magnitude and direction of each pixel in the image. The gradient direction indicates the direction in which the pixel grayscale value changes fastest, while the gradient magnitude indicates the degree of change in that direction. In the Canny algorithm, methods such as the Sobel operator can be used to calculate the gradient.

[0069] 3. Non-maximum suppression. After calculating the gradient magnitude and direction, the edges in the image may still be very wide and need to be refined. Non-maximum suppression is a method that traverses each pixel in the gradient magnitude image. If the gradient magnitude of the pixel is not the largest in its gradient direction, it is set to zero. This can retain the points with the largest local gradient, that is, the edge points.

[0070] 4. Double threshold detection. After performing non-maximum suppression, it is necessary to determine which edges are real edges and which are noise or weak edges. The Canny algorithm uses a double threshold method to achieve this. It sets two thresholds: a high threshold and a low threshold. If the gradient amplitude of a pixel is higher than the high threshold, it is marked as a strong edge pixel. If it is lower than the low threshold, it is suppressed. If it is between the two, it is marked as a weak edge pixel.

[0071] 5. Hysteresis threshold, for pixels marked as weak edges, if they are connected to strong edge pixels, they are retained as edges, otherwise they are suppressed, which ensures that the detected edges are continuous and removes isolated noise points.

[0072] The above steps of the Canny edge detection algorithm together ensure that the detected edges have low error rate, accurate positioning and clear response. Figure 7 As shown, the wireframe graphics after noise removal and morphological reconstruction.

[0073] 4) Calculate the approximate portion of the ground contour line and the parking space contour line, extract the pixel point coordinates, and obtain the parking space contour line adjusted by the ground contour line.

[0074] like Figure 8 and Figure 9 As shown, in order to solve the problem of incomplete parking space wireframes in the image caused by years of wind and rain, it is necessary to extract the contour line of the ground part and determine the planning of the parking space wireframe by selecting the intersection of the ground and the parking space wireframe. The specific method is to compare Figure 7 The coordinates of the parking space wireframe contour extracted by the Canny algorithm and Figure 9 The ground contour line coordinates extracted from the , set a suitable threshold, traverse all the coordinate points of the two, if the threshold is exceeded, it is judged not close, otherwise it is judged close. The filtered parking space wireframe contour line coordinates close to the ground contour line coordinates are as follows Figure 10 After screening, the coordinates of the actual parking space contour lines will be mostly retained, and the larger noise blocks that were not removed when removing noise points will be removed in this step, as shown in Figure 11 shown.

[0075] 5) Search the license plate location in the image and mark it in the parking space line contour image in step 4). Figure 12 As shown, the license plate position is searched in the image by the "license plate search algorithm". The license plate search algorithm in the prior art can be used in the present invention, and its image and pixel coordinates are captured and marked according to its pixel coordinates. Figure 12 The result is as follows Figure 13 shown.

[0076] 6) By calculating the intersection of the normal vector equation of the license plate to the road surface and the parking space direction equation, the relative position of the vehicle and the parking space can be determined. Figure 14 As shown, the present invention performs cluster analysis on the contour lines through the Gaussian mixture clustering algorithm to obtain and distinguish the categories of various parts of the contour lines. The red and blue categories in the figure are the two sides of the contour lines respectively. For the edges of the different parking space frames after clustering, their slope and intercept coefficients are solved according to linear programming, and the direction of their wireframes is represented by a straight line. For example, the direction of the blue category is represented by a black line, and the direction of the red wireframe is represented by a green line. The average direction of the black and green lines is the orientation of the parking space, represented by a thin red line. The green far point represents the position of the license plate, and the normal vector ray is drawn through the license plate to the plane defined by the black and green lines. The light blue straight line is the normal vector ray. The wireframe size of the parking space contains value range information. After the aforementioned steps, we have obtained the equation of the light blue normal vector ray and the equation of the thin red line, as shown Figure 15 As shown, at this time, solve the two straight line equations. If the system of equations has an intersection solution and the intersection solution is within the value range, the vehicle stays in the parking space, otherwise it is outside the parking space.

[0077] At this point, the algorithm solves the position of the vehicle relative to the parking space.

[0078] The method provided by the present invention is based on the image shooting detection method. After a series of steps, the normal vector equation of the license plate to the road surface and the parking space direction equation are obtained. The relative position of the vehicle and the parking space is determined by solving the intersection solution of the equation group composed of the two equations. This effectively reduces the influence of adverse factors such as different vehicle models, lighting conditions, and obstructions, effectively reduces the computational complexity of determining the parking position analysis of roadside vehicles, greatly reduces the demand for computing resources, and can achieve high-efficiency parking space detection and retrieval, effectively realizing the maximization of parking space resource utilization. During the solution process, there is no need to install additional equipment for the parking spaces where roadside parked vehicles are located, thereby reducing costs.

[0079] In another embodiment, the present invention further provides a system for identifying irregular parking on a road, which determines a roadside parking space based on the aforementioned irregular parking identification method.

[0080] In another embodiment, the present invention also provides a computer device, including a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the computer device performs the steps of implementing the above method.

[0081] In another embodiment, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

Claims

1. A method for identifying irregular parking on a road, characterized in that: The following steps are involved: 1) Read the image and convert the RGB image to HSV space; 2) Process the HSV space and reproduce the approximate position of the parking space line; 3) For parking space line extraction, remove noise, perform morphological reconstruction, and obtain the parking space line contour; 4) Calculate the approximate portion of the ground contour line and the parking space contour line, extract the pixel point coordinates, and obtain the parking space contour line adjusted by the ground contour line; 5) Search for the license plate position in the image and mark it in the parking space line contour image in step 4); 6) Perform cluster analysis on the contour lines, calculate the intersection of the normal vector equation of the license plate to the road surface and the parking space direction equation, and determine whether the intersection solution is within the value range, thereby determining the relative position of the vehicle and the parking space; The step 6) specifically includes: performing cluster analysis on the contour lines to obtain categories of various parts of the contour lines, solving the slope and intercept coefficient of the edges of the clustered parking space frames according to linear programming, representing the direction of the wireframe by a straight line, and using the average direction of the wireframe direction as the parking space direction to obtain the parking space direction equation; the wireframe size of the parking space includes value range information; the normal vector equation of the license plate to the road surface is generated through the direction of the license plate to the wireframe; solving the equation system consisting of the normal vector equation of the license plate to the road surface and the parking space direction equation; if the equation system has an intersection solution and the intersection solution is within the value range, the vehicle is located in the parking space; otherwise, the vehicle is located outside the parking space, thereby determining the relative position of the vehicle and the parking space.

2. The method for identifying irregular parking on a road according to claim 1, characterized in that: In the step 1), the image reading and converting the RGB image into the HSV space specifically includes: collecting an image in the parking area through a camera as an input for detection; after the image is loaded, the matrix data of the workspace is returned; and converting the obtained RGB image into the HSV space through a series of mathematical calculations.

3. The method for identifying irregular parking on a road according to claim 2, characterized in that: In the step 2), the ground and the parking space wireframe are generally identified based on the obtained HSV component map, and the approximate positions of the ground and the parking space wireframe are determined, and finally copied into the blank image.

4. The method for identifying irregular parking on a road according to claim 3, characterized in that: In the step 3), obtaining the parking space line contour includes: calling the Canny edge detection algorithm to accurately detect the image edge through Gaussian blur denoising, calculating gradient amplitude and direction, non-maximum suppression, dual threshold detection and hysteresis threshold.

5. The method for identifying irregular parking on a road according to claim 4, characterized in that: The step 4) includes intercepting the ground contour and extracting the ground contour line.

6. The method for identifying irregular parking on a road according to claim 5, characterized in that: The step 5) specifically includes: searching for the license plate position in the image through the "license plate search algorithm", capturing its image and pixel coordinates, and marking it in the parking space line contour image of step 4) according to its pixel coordinates.

7. Road irregular parking recognition system, characterized by: The system determines the parking space and the relative position of the parking space based on the method described in any one of claims 1 to 6.

8. A computer device, characterized in that: The computer device comprises a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the computer device executes the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 6 is implemented.

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