Freight vehicle roadside illegal parking detection method and system based on image recognition

Abstract

The invention relates to the technical field of vehicle illegal parking detection, and discloses a freight vehicle roadside illegal parking detection method and system based on image recognition, and the method comprises the steps: obtaining the GPS point location information of a detected vehicle, mapping the GPS point location information to a road network map, and calibrating the vehicle label information; constructing a freight vehicle roadside illegal parking detection model, and obtaining a freight vehicle roadside illegal parking detection result; and if the roadside illegal parking detection result of the freight vehicle is a roadside illegal parking state, obtaining feature information of the freight vehicle, calculating the occurrence frequency of illegal parking of the freight vehicle on a road, and generating a roadside illegal parking high-risk area of the freight vehicle. According to the invention, the roadside illegal parking behavior of the freight vehicle can be efficiently identified in a complex traffic environment, and the management and control efficiency and operability of roadside illegal parking detection of the freight vehicle are improved through automatic detection of the roadside illegal parking behavior and accurate road roadside illegal parking risk assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle illegal parking detection, and particularly to a method and system for detecting roadside illegal parking of freight vehicles based on image recognition. Background Art

[0002] With the rapid development of urbanization and freight transportation, the illegal parking behavior of freight vehicles has become one of the persistent problems in urban management. Especially in complex environments such as districts, remote areas, and where road data is lacking, traditional detection technologies that rely on complete background data are difficult to play a role. This not only leads to the existence of traffic management blind spots but also lays a hidden danger for the occurrence of serious traffic accidents.

[0003] Due to their large size and complex parking requirements, freight vehicles often occupy more road resources when parking. Once a roadside illegal parking behavior occurs, it will seriously hinder the normal passage of other vehicles and even trigger serious traffic accidents such as multi-vehicle chain collisions. Most of the existing illegal parking detection technologies based on road video surveillance rely on complete electronic maps and road boundary information. However, in cases where regional information is insufficient or data is incomplete, the accuracy and reliability of the model often drop significantly, and there are bottlenecks such as a high false positive rate and many missed detection problems. Moreover, most of the existing methods identify illegal parking phenomena at specific points and cannot give a macro view of the high-incidence areas of roadside illegal parking of freight vehicles, so as to optimize police deployment and conduct targeted governance for high-risk areas of roadside illegal parking.

[0004] The prior art proposes an algorithm for detecting illegal parking on the sidewalk based on deep learning and semantic segmentation for detecting illegal parking on the sidewalk based on object detection and semantic segmentation. However, the limitation of this method is that it can only identify whether a vehicle is parked on the sidewalk, and its applicable range is relatively limited. In addition, during the semantic segmentation process of the image, the image is only divided into three categories: road, sidewalk, and car, and the classification method is relatively simple. As a result, in a complex urban road environment, the accuracy and processing speed of the segmentation still need to be further improved. In addition, there is no license plate recognition function, so subsequent penalties cannot be imposed on illegally parked vehicles.

[0005] The prior art proposes a method for detecting illegal parking of motor vehicles from a high perspective in a complex environment. A method for detecting illegal parking of motor vehicles from a high perspective is proposed, which is based on an improved YOLOv3-TINY backbone network and an attention mechanism for detecting illegal parking of motor vehicles. However, the limitations of this method are as follows: its application scenario is limited to high-perspective monitoring, ignoring the potential needs of low-perspective or dynamic monitoring; it relies strongly on fixed-mounted high-position monitoring devices, and the implementation conditions are harsh, which is not suitable for areas with limited resources or insufficient infrastructure; in addition, although the model structure has been optimized, the detection accuracy in complex scenarios (such as dense traffic, severe occlusion, etc.) still needs to be verified. Summary of the Invention

[0006] In view of the existing problems mentioned above, the present invention is proposed. Therefore, the present invention provides a method for detecting roadside illegal parking of freight vehicles based on image recognition to solve the problems of how to efficiently and accurately identify the roadside illegal parking behavior of freight vehicles in a complex traffic environment, the lack of means for supervising the roadside illegal parking of freight vehicles, low efficiency and low accuracy.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for detecting roadside illegal parking of freight vehicles based on image recognition, including: obtaining the GPS point information of the detected vehicle, mapping it to the road network map, and calibrating the vehicle label information; Constructing a detection model for illegal roadside parking of freight vehicles, extracting key frame images of roadside parking from the road network map containing the parking points, and inputting them into the detection model for illegal roadside parking of freight vehicles for processing to obtain the detection result of illegal roadside parking of freight vehicles; If the detection result of the illegal roadside parking of the freight vehicle is the roadside illegal parking state, according to the vehicle label in the key frame image of the roadside parking, obtaining the characteristic information of the freight vehicle, calculating the occurrence frequency of illegal parking of trucks on the road, sorting the occurrence frequency of roadside illegal parking from high to low, and generating a high-risk area for illegal roadside parking of freight vehicles.

[0008] As a preferred solution of the method for detecting roadside illegal parking of freight vehicles based on image recognition according to the present invention, wherein: obtaining the GPS point information of the detected vehicle, mapping it to the road network map, and calibrating the vehicle label information includes: Obtaining the GPS point information of the detected vehicle through the vehicle's own GPS positioning module, and the GPS point information includes the longitude, latitude, timestamp, and license plate number of the vehicle's parking position; Mapping the real-time position of the vehicle to the road network information in the traffic management system to determine the specific position of the vehicle.

[0009] As a preferred solution of the method for detecting roadside illegal parking of freight vehicles based on image recognition according to the present invention, wherein: constructing a detection model for illegal roadside parking of freight vehicles includes: Based on the improved MTP multi-task pre-training semantic segmentation algorithm, constructing a detection model for illegal roadside parking of freight vehicles, and identifying and segmenting the road layer and parking points through training the model; Calculating the distance between the target parking point and the center line, and comparing it with the preset tolerance value to determine whether the vehicle parks within the roadside range; If the distance between the parking point and the center line is less than the tolerance value and it is located in the roadside no-parking area, it is determined as the roadside illegal parking state.

[0010] As a preferred solution of the roadside illegal parking detection method for freight vehicles based on image recognition according to the present invention, wherein: calculating the distance between the target parking point and the center line and comparing it with a preset tolerance value specifically includes: Calculating the relative position of the vehicle point and the road center line, and setting the GPS point of the vehicle as , finding the tangent line equation of the road center line , the distance between the vehicle and the road center line is expressed as: ; wherein, , represent the coefficients of the center line straight line equation, represents the intercept; Evaluating the deviation tolerance value. If the calculated distance between the vehicle and the road center line is greater than the preset tolerance value , it is determined that the parking point does not belong to the roadside illegal parking range; if the distance between the vehicle and the road center line is less than or equal to the preset tolerance value , it is determined as a high-risk roadside illegal parking.

[0011] As a preferred solution of the roadside illegal parking detection method for freight vehicles based on image recognition according to the present invention, wherein: obtaining the detection result of the freight vehicle's roadside illegal parking includes: Dividing the detection result of the freight vehicle's roadside illegal parking into a complete roadside illegal parking state, a high-risk roadside illegal parking state, and a non-roadside illegal parking state. Among them, the complete roadside illegal parking state and the high-risk roadside illegal parking state are determined as the roadside illegal parking state; Among them, by comparing from the key frame images of the roadside parking, if the freight vehicle stops within the road area within the road width from the center line, it is determined as the complete roadside illegal parking state; If the freight vehicle stops within the area from the road width to the tolerance value from the center line, it is determined as the high-risk roadside illegal parking state; If the parking position is greater than the tolerance value range, or the parking behavior does not violate the roadside regulations, it is determined as the non-roadside illegal parking state.

[0012] As a preferred solution of the roadside illegal parking detection method for freight vehicles based on image recognition according to the present invention, wherein: generating a high-risk area for the freight vehicle's roadside illegal parking includes: According to the complete roadside illegal parking state, the high-risk roadside illegal parking state, and the non-roadside illegal parking state, counting the occurrence times of the freight vehicle's illegal parking on the road and calculating the occurrence frequency of the freight vehicle's illegal parking; Based on the occurrence frequency of illegal parking of freight vehicles, comprehensively evaluate the roads, sort the roads according to the illegal parking risk from high to low, generate a list of high-risk areas for roadside illegal parking of freight vehicles based on the risk ranking results, and at the same time mark the risk rankings of each road in the three types of full roadside illegal parking state, high-risk roadside illegal parking state and non-roadside illegal parking state, and generate a high-risk area distribution map centered on the road.

[0013] As a preferred solution of the method for detecting roadside illegal parking of freight vehicles based on image recognition according to the present invention, wherein: extract the illegal parking aggregation area in the image of illegal parking of freight vehicles on the roadside, input it into the road area recognition model, and recognize the road name and the area to which it belongs in the case of missing road background data; Obtain the license plate number and aggregation area of the illegally parked vehicle, upload the license plate number and area information to the cloud to determine the driver identity information of the illegally parked vehicle, and perform key risk control processing on this section of the road according to the high-risk area of illegal parking.

[0014] In a second aspect, the present invention provides a system for detecting roadside illegal parking of freight vehicles based on image recognition, including: An acquisition module for obtaining the GPS point information of the detected vehicle, mapping it to the road network map, and calibrating the vehicle label information; A detection module for constructing a detection model for illegal parking of freight vehicles on the roadside, extracting key frame images of roadside parking from the road network map containing the parking point, and inputting them into the detection model for illegal parking of freight vehicles on the roadside for processing to obtain the detection result of illegal parking of freight vehicles on the roadside; An output module for, when the detection result of illegal parking of freight vehicles on the roadside is the roadside illegal parking state, obtaining the characteristic information of the freight vehicle according to the vehicle label in the key frame image of roadside parking, calculating the occurrence frequency of illegal parking of freight vehicles on the road, sorting the occurrence frequency of road illegal parking from high to low, and generating a high-risk area for roadside illegal parking of freight vehicles.

[0015] In a third aspect, the present invention provides an electronic device, including: A memory and a processor; The memory is used for storing computer-executable instructions, and the processor is used for executing the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the method for detecting roadside illegal parking of freight vehicles based on image recognition are realized.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps of the method for detecting roadside illegal parking of freight vehicles based on image recognition are realized.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention can efficiently identify the roadside illegal parking behavior of freight vehicles in a complex traffic environment. Through the automatic detection of roadside illegal parking behavior and the accurate risk assessment of roadside illegal parking on roads, it optimizes the illegal parking management and police force deployment, improves the control efficiency and operability of the detection of illegal roadside parking of freight vehicles, and has a high level of intelligence and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic diagram of the overall process of the method for detecting roadside illegal parking of freight vehicles based on image recognition according to an embodiment of the present invention; Figure 2 It is a schematic diagram of the main working process of the method for detecting roadside illegal parking of freight vehicles based on image recognition according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention should fall within the protection scope of the present invention.

[0021] Referring to Figure 1 - Figure 2 , an embodiment of the present invention provides a method for detecting roadside illegal parking of freight vehicles based on image recognition, including: S100, obtaining the GPS point information of the detected vehicle, mapping it to the road network map, and calibrating the vehicle label information; S200, constructing a detection model for illegal roadside parking of freight vehicles, extracting key frame images of roadside parking from the road network map containing parking points, and inputting them into the detection model for illegal roadside parking of freight vehicles for processing to obtain the detection result of illegal roadside parking of freight vehicles; S300, when the detection result of illegal roadside parking of freight vehicles is the roadside illegal parking state, obtaining the feature information of the freight vehicle according to the vehicle label in the key frame image of roadside parking, calculating the occurrence frequency of illegal parking of trucks on the road, sorting the occurrence frequency of roadside illegal parking from high to low, and generating a high-risk area for illegal roadside parking of freight vehicles.

[0022] It should be noted that the present invention proposes a comprehensive solution that combines GPS points, image recognition technology and deep learning algorithms. It can evaluate whether a parking spot belongs to the roadside in the absence of road background data or information. According to the roadside illegal parking assessment results, the frequency of illegal parking of trucks on the road is counted and sorted, and a high-risk area for roadside illegal parking is generated. Targeted management and control are carried out in the high-risk area, and accurate detection of illegal parking of freight vehicles on the roadside and dynamic intelligent management of high-risk areas are achieved. It can realize automated detection and accurate risk assessment, optimize illegal parking management and police deployment, and complete roadside illegal parking detection and monitoring.

[0023] Preferably, the GPS location information of the detected vehicle is obtained and mapped to a road network map, and the vehicle tag information is calibrated including: The GPS location information of the detected vehicle is obtained through the vehicle's own GPS positioning module. The GPS location information includes the longitude, latitude, timestamp, and license plate number of the vehicle's parking location; The real-time location of the vehicle is mapped with the road network information in the traffic management system to determine the specific location of the vehicle.

[0024] Specifically, in this embodiment, the roadside is defined as the space extending from the curb to the inside to the lane. This step can collect the precise positioning data and time information of the vehicle in real time, provide the basic spatial and temporal dimension basis for the subsequent illegal parking judgment, and ensure the timeliness and data integrity of the detection request; map the vehicle GPS point information to the road network map, calibrate the vehicle label and other information, mainly rely on map matching technology and geographic information system, map matching is combined with the geographic information system through geometric matching, topological matching or probability matching algorithm based on hidden Markov model, to accurately align discrete GPS points to the road network, provide a basis for subsequent analysis and decision-making, align discrete GPS data with the road network topology through the geographic information system, realize accurate matching and visual annotation of vehicle positions, provide geographic spatial correlation support for subsequent image capture and road boundary analysis, capture key frame images of the map containing parking points and upload them to the cloud, optimize data transmission efficiency, reduce redundant image processing burden through key frame extraction, and use cloud storage and computing resources to realize rapid centralized processing and analysis of large-scale image data.

[0025] Preferably, constructing a freight vehicle roadside illegal parking detection model includes: Based on the improved MTP multi-task pre-training semantic segmentation algorithm, a freight vehicle roadside illegal parking detection model is constructed, and the road layer and parking points are identified and segmented through the training model; By calculating the distance between the target parking point and the center line and comparing it with the preset tolerance value, it is determined whether the vehicle is parked within the roadside range; If the distance between the parking point and the center line is less than the tolerance value and it is located within the roadside no-parking area, it is determined to be in a roadside illegal parking state.

[0026] Specifically, for the roadside illegal parking detection model of freight vehicles, based on the semantic segmentation of the potential illegal parking point map of MTP, it includes: Multi-task annotation preparation: Label the map image with the label set required for semantic segmentation, including: road area (segmented road layer), parking point (marked point), road center line (generate center line annotation through geometric transformation without a center line), and use the SAMRS dataset or similar annotation tools to generate semantic segmentation labels.

[0027] Feature extraction and pyramid construction: Use a pre-trained model to extract multi-scale features of the image and construct a feature pyramid. The features of different resolution layers are input into the semantic segmentation decoder.

[0028] Semantic segmentation task head: In the semantic segmentation decoder, classify each pixel into different categories (road, parking point, background, etc.) according to the input features, and use the task loss function to optimize the segmentation result.

[0029] Training and optimization: Conduct multi-task pre-training on the training dataset, and perform joint optimization by combining the loss functions of other tasks to enhance the segmentation ability of the model.

[0030] Model migration and application: After pre-training, migrate the model to a specific task, fine-tune the model to adapt to specific road segmentation and parking point detection tasks. In the output result, the road area is marked by the segmentation mask, the parking point is identified through classification or region detection, and the center line can be further generated through post-processing (such as extracting the midline of the road segmentation boundary).

[0031] Preferably, calculating the distance between the target parking point and the center line and comparing it with the preset tolerance value specifically includes: Calculate the relative position of the vehicle point to the road center line. Let the GPS point of the vehicle be and find the tangent line equation of the road center line The distance between the vehicle and the road center line is expressed as: ; where and represent the coefficients of the center line straight line equation, represents the intercept to quantify the deviation degree of the vehicle relative to the center line; Conduct an evaluation of the deviation tolerance value. If the calculated distance between the vehicle and the road center line is greater than the preset tolerance value , it is determined that this parking spot does not belong to the roadside illegal parking range; if the distance between the vehicle and the road center line is less than or equal to a preset tolerance value , it is determined as a high-risk roadside illegal parking.

[0032] In an alternative embodiment, the tolerance value is a preset dynamically adjustable parameter tolerance value, used for evaluating the possibility of whether the vehicle is located within the roadside parking area. The calculation formula is as follows: ; where is the resolution of the map image, is the GPS positioning accuracy, is the influencing factor of real-time traffic flow or historical illegal parking records.

[0033] It should be noted that in this embodiment, the roadside illegal parking detection model for freight vehicles is built based on the improved MTP multi-task pre-trained semantic segmentation technology. By using a large-scale remote sensing dataset in the pre-training stage, combined with the cosine annealing scheduling of the learning rate and hierarchical decay to optimize the performance of the large model, it shows excellent performance in specific semantic segmentation tasks. The improved MTP multi-task pre-trained semantic segmentation technology solves the differences between upstream pre-training and downstream fine-tuning tasks by introducing a staged multi-task pre-training method; by pre-training the basic model, it integrates semantic segmentation and object detection tasks in one framework. Its deep learning-based image recognition model automatically analyzes the illegal parking scene, combined with the road boundary semantic segmentation technology, to improve the accuracy and robustness of illegal parking detection in complex environments and reduce the need for manual intervention.

[0034] Preferably, obtaining the roadside illegal parking detection result for freight vehicles includes: Dividing the roadside illegal parking detection result for freight vehicles into a complete roadside illegal parking state, a high-risk roadside illegal parking state, and a non-roadside illegal parking state. Among them, the complete roadside illegal parking state and the high-risk roadside illegal parking state are determined as the roadside illegal parking state; Among them, by comparing from the key frame images of roadside parking, if the freight vehicle stops within the road area within the road width from the center line, it is determined as the complete roadside illegal parking state; If the freight vehicle stops within the area from the road width to the tolerance value from the center line, it is determined as the high-risk roadside illegal parking state; If the parking position is greater than the tolerance value range, or the parking behavior does not violate the roadside regulations, it is determined as the non-roadside illegal parking state.

[0035] It should be noted that the characteristic information such as the license plate of the freight vehicle is determined based on the vehicle label in the parking keyframe image, and the license plate recognition technology (such as OCR) is integrated to accurately extract the identity of the illegal vehicle, provide reliable data support for subsequent law enforcement evidence collection and violation tracing, generate high-risk areas for illegal parking on the roadside of freight vehicles, construct illegal parking heat maps and risk distribution models based on spatiotemporal data analysis, identify high-incidence sections, and assist traffic management departments in optimizing police force deployment and monitoring equipment configuration.

[0036] Preferably, generating a high-risk area for illegal parking of freight vehicles on the roadside includes: According to the complete roadside illegal parking status, high-risk roadside illegal parking status and non-roadside illegal parking status, the number of illegal parking of freight vehicles on the road is counted, and the frequency of illegal parking of trucks is calculated; A comprehensive assessment of roads is conducted based on the frequency of illegal parking of trucks, and roads are ranked from high to low according to the risk of illegal parking. A list of high-risk areas for roadside illegal parking of freight vehicles is generated based on the risk ranking results. At the same time, the risk ranking of each road in three types: complete roadside illegal parking state, high-risk roadside illegal parking state, and non-roadside illegal parking state is marked to generate a high-risk area distribution map with roads as the core.

[0037] Preferably, the illegal parking cluster area in the roadside illegal parking image of freight vehicles is extracted and input into the road area recognition model to identify the road name and the area to which it belongs when the road background data is missing. Through image semantic segmentation and text recognition (OCR) technology, the road section attributes (such as road names, landmarks) are restored when the road information is missing, thereby enhancing the adaptability and reliability of the system in scenarios with incomplete data; Obtain the license plate numbers and gathering areas of illegally parked vehicles, upload the license plate numbers and area information to the cloud to determine the identity information of the drivers of illegally parked vehicles, and conduct key risk management and control on the road section according to the high-risk area for illegal parking, to achieve "data-analysis-disposal" closed-loop management, trigger targeted law enforcement through identity association and area association (such as automatically notifying law enforcement personnel or initiating parking restrictions), and dynamically adjust the control strategies for high-risk areas to improve traffic management efficiency.

[0038] Optionally, when the detected roadside illegally parked vehicles are located in a high-incidence area, the area is subject to key risk management and control. Specific management and control measures include but are not limited to: Automatically notify traffic enforcement officers to conduct on-site inspections; Carry out key traffic control in areas with high incidence of illegal parking, such as restricting parking during certain periods of time or increasing the deployment of monitoring equipment.

[0039] Optionally, for the situation where the road background information in some areas is missing and it is impossible to match the high-risk roadside illegal parking situation to a specific road section, this embodiment proposes a road area recognition model. By extracting the geographical feature information in the map image, determining the specific illegal parking section, constructing a road area recognition model, and extracting the area information in the map image, the following steps are included: Image scanning and area positioning: First, use image processing technology to scan the map image, and locate the road area and relevant label information through edge detection algorithms.

[0040] Text area recognition and label extraction: Extract the text area in the image through image segmentation methods, and use OCR technology to recognize the text labels in the image, such as road names, landmarks, shops, etc.

[0041] Matching and position calculation: Match the text content extracted by OCR with the coordinate data of the target area to generate structured data containing road names and area labels for further analysis.

[0042] Furthermore, the detailed principle process of image text recognition includes: Image preprocessing: Perform grayscale processing on the map image, use Gaussian blur to remove noise, make the image smoother, and improve the accuracy of text recognition; OCR technology: Perform text recognition to recognize information such as road names and geographical labels in the image.

[0043] It should be noted that the present invention can efficiently identify the roadside illegal parking behavior of freight vehicles in a complex traffic environment. Through the automated detection of roadside illegal parking behavior and the accurate risk assessment of roadside illegal parking on roads, it optimizes illegal parking management and police force deployment, improves the control efficiency and operability of detecting illegal parking of freight vehicles on the roadside, has a high level of intelligence and application prospects, and is especially suitable for roadside illegal parking detection and monitoring in intelligent transportation management systems.

[0044] The above is a schematic solution of a method for detecting roadside illegal parking of freight vehicles based on image recognition in this embodiment. It should be noted that the technical solution of the system for detecting roadside illegal parking of freight vehicles based on image recognition belongs to the same concept as the technical solution of the above-mentioned method for detecting roadside illegal parking of freight vehicles based on image recognition. For the details not described in the technical solution of the system for detecting roadside illegal parking of freight vehicles based on image recognition in this embodiment, reference can be made to the description of the technical solution of the method for detecting roadside illegal parking of freight vehicles based on image recognition.

[0045] A system for detecting roadside illegal parking of freight vehicles based on image recognition in this embodiment includes: An acquisition module, configured to obtain the GPS point information of the detected vehicle, map it to the road network map, and calibrate the vehicle label information; A detection module, configured to build a roadside illegal parking detection model for freight vehicles, extract key frame images of roadside parking from a road network map containing parking points, and input them into the roadside illegal parking detection model for freight vehicles for processing to obtain the roadside illegal parking detection result of freight vehicles; An output module, configured to, when the roadside illegal parking detection result of the freight vehicle is in a roadside illegal parking state, obtain the feature information of the freight vehicle according to the vehicle label in the key frame image of the roadside parking, calculate the occurrence frequency of illegal parking of trucks on the road, sort the occurrence frequencies of road illegal parking from high to low, and generate a high-risk area for roadside illegal parking of freight vehicles.

[0046] This embodiment also provides an electronic device, applicable to the situation of roadside illegal parking detection of freight vehicles based on image recognition, including: A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for roadside illegal parking detection of freight vehicles based on image recognition as proposed in the above embodiment.

[0047] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the method for roadside illegal parking detection of freight vehicles based on image recognition as proposed in the above embodiment.

[0048] The storage medium proposed in this embodiment and the method for roadside illegal parking detection of freight vehicles based on image recognition proposed in the above embodiment belong to the same inventive concept. For technical details not described in detail in this embodiment, reference can be made to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0049] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general-purpose hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (FLASH), a hard disk, or an optical disc of a computer, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments of the present invention.

[0050] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for detecting illegal parking of freight vehicles on the roadside based on image recognition, characterized in that: include: Obtain the GPS location information of the detected vehicle, map it to the road network map, and calibrate the vehicle label information; Construct a freight vehicle roadside illegal parking detection model, extract roadside parking key frame images from the road network map containing parking spots, input them into the freight vehicle roadside illegal parking detection model for processing, and obtain freight vehicle roadside illegal parking detection results; If the detection result of the freight vehicle's roadside illegal parking is a roadside illegal parking state, the characteristic information of the freight vehicle is obtained according to the vehicle label in the roadside parking key frame image, and the frequency of illegal parking of trucks on the road is calculated, and the frequency of illegal parking on the road is sorted from high to low to generate a high-risk area for illegal parking on the roadside of freight vehicles.

2. The method for detecting illegal parking of freight vehicles on the roadside based on image recognition as claimed in claim 1, characterized in that: Obtain the GPS location information of the detected vehicle and map it to the road network map. The calibrated vehicle label information includes: The GPS location information of the detected vehicle is obtained through the vehicle's own GPS positioning module, and the GPS location information includes the longitude, latitude, timestamp, and license plate number of the vehicle's parking location; The real-time location of the vehicle is mapped with the road network information in the traffic management system to determine the specific location of the vehicle.

3. A method for detecting illegal parking of freight vehicles on the roadside based on image recognition as claimed in claim 2, characterized in that: Building a model for detecting illegal roadside parking of freight vehicles includes: Based on the improved MTP multi-task pre-training semantic segmentation algorithm, a freight vehicle roadside illegal parking detection model is constructed, and the road layer and parking points are identified and segmented through the training model; By calculating the distance between the target parking point and the center line and comparing it with the preset tolerance value, it is determined whether the vehicle is parked within the roadside range; If the distance between the parking point and the center line is less than the tolerance value and is located in the roadside prohibited parking area, it will be judged as a roadside illegal parking state.

4. A method for detecting illegal parking of freight vehicles on the roadside based on image recognition as claimed in claim 3, characterized in that: The step of calculating the distance between the target parking point and the center line and comparing the distance with the preset tolerance value specifically includes: Calculate the relative position of the vehicle point and the center line of the road. Suppose the GPS point of the vehicle is , find the equation of the tangent line to the center line of the road , the distance between the vehicle and the center line of the road It is expressed as: ； in, , represents the coefficient of the equation of the center line, represents the intercept; The deviation tolerance value is evaluated. If the calculated distance between the vehicle and the center line of the road Greater than the preset tolerance value , then it is determined that the parking spot does not belong to the roadside illegal parking range; if the distance between the vehicle and the center line of the road Less than or equal to the preset tolerance value , it is judged as high-risk roadside illegal parking.

5. The method for detecting illegal parking of freight vehicles on the roadside based on image recognition as claimed in claim 4, characterized in that: Obtaining the detection results of illegal roadside parking of freight vehicles includes: The detection result of the roadside illegal parking of the freight vehicle is divided into a complete roadside illegal parking state, a high-risk roadside illegal parking state and a non-roadside illegal parking state, wherein the complete roadside illegal parking state and the high-risk roadside illegal parking state are determined as the roadside illegal parking state; Among them, a comparison is made from the roadside parking key frame image. If the freight vehicle stops at a distance from the center line If the vehicle is parked within the road width, it will be considered as illegal roadside parking. If the freight vehicle stops within a certain distance of the center line If the road width is within the tolerance value area, it will be judged as a high-risk roadside illegal parking state; If the parking position is larger than the tolerance value range, or the parking behavior does not violate the roadside regulations, it will be judged as non-roadside illegal parking.

6. A method for detecting illegal parking of freight vehicles on the roadside based on image recognition as claimed in claim 5, characterized in that: The high-risk areas for illegal parking of freight vehicles on the roadside include: According to the complete roadside illegal parking status, high-risk roadside illegal parking status and non-roadside illegal parking status, the number of illegal parking of freight vehicles on the road is counted, and the frequency of illegal parking of trucks is calculated; A comprehensive assessment of roads is conducted based on the frequency of illegal parking of trucks, and roads are ranked from high to low according to the risk of illegal parking. A list of high-risk areas for roadside illegal parking of freight vehicles is generated based on the risk ranking results. At the same time, the risk ranking of each road in the three types of complete roadside illegal parking state, high-risk roadside illegal parking state and non-roadside illegal parking state is marked to generate a high-risk area distribution map with roads as the core.

7. A method for detecting illegal parking of freight vehicles on the roadside based on image recognition as claimed in claim 6, characterized in that: Extract the illegal parking cluster area in the roadside illegal parking image of freight vehicles, input it into the road area recognition model, and identify the road name and area in the absence of road background data; Obtain the license plate numbers and gathering areas of illegally parked vehicles, upload the license plate numbers and area information to the cloud to determine the identity information of the drivers of illegally parked vehicles, and conduct key risk management and control on the road section based on the high-risk area for illegal parking.

8. A freight vehicle roadside illegal parking detection system based on image recognition, using a freight vehicle roadside illegal parking detection method based on image recognition as claimed in any one of claims 1 to 7, characterized in that: include, The acquisition module is used to obtain the GPS location information of the detected vehicle, map it to the road network map, and calibrate the vehicle label information; A detection module is used to construct a freight vehicle roadside illegal parking detection model, extract roadside parking key frame images from the road network map containing parking spots, input them into the freight vehicle roadside illegal parking detection model for processing, and obtain freight vehicle roadside illegal parking detection results; The output module is used to obtain the characteristic information of the freight vehicle according to the vehicle label in the roadside parking key frame image if the detection result of the freight vehicle's roadside illegal parking is a roadside illegal parking state, and calculate the frequency of illegal parking of trucks on the road, sort the frequency of illegal parking on the road from high to low, and generate a high-risk area for illegal parking of freight vehicles on the roadside.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of a method for detecting illegal roadside parking of freight vehicles based on image recognition as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, can implement the steps of a method for detecting illegal roadside parking of freight vehicles based on image recognition as described in any one of claims 1 to 7.

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