A highway monitoring image enhancement method and system

Abstract

The present invention provides a method and system for enhancing highway monitoring images, which relates to the field of monitoring image processing technology, including: based on lane change marking points, traffic flow management is performed according to the initial partition of the highway, and traffic flow management coordinates are set; based on the road surface marking section, traffic risk management is performed according to the initial partition of the vehicle, and traffic risk management sections are set; according to the traffic flow management coordinates and the initial partition of the highway, the traffic risk management section and the initial partition of the vehicle, the image processing parameters are matched to perform monitoring image enhancement processing. The present invention can solve the technical problem that the existing method cannot set matching image processing parameters according to the actual road conditions in the vehicle diversion section, resulting in poor monitoring effect when dealing with complex traffic conditions, affecting the safety and traffic efficiency of vehicle diversion, and can achieve the goal of accurate monitoring of vehicles in the diversion section, and achieve the technical effect of improving the safety and traffic efficiency of vehicle diversion.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring image processing, and in particular to a method and system for enhancing monitoring images of expressways. Background Art

[0002] With the continuous growth of traffic volume, highway management faces more and more challenges, especially in vehicle diversion sections. These sections not only have large traffic volumes, but also have more complex traffic conditions due to factors such as frequent lane changes and large speed changes, which increases the difficulty of management. In order to effectively manage these key sections, highway monitoring systems usually rely on cameras and other monitoring equipment deployed on the roadside to obtain real-time image data of vehicles and road conditions. These image data are crucial for traffic flow management, accident warning, emergency response, etc. However, existing highway vehicle monitoring methods usually use fixed image processing parameters, which have certain limitations when dealing with complex road conditions such as vehicle diversion.

[0003] At present, there are technical problems in the existing highway vehicle monitoring methods, such as the inability to set matching image processing parameters according to actual road conditions in vehicle diversion sections, resulting in poor monitoring effects when dealing with complex traffic conditions, affecting vehicle diversion safety and traffic efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a highway monitoring image enhancement method and system to solve the technical problem that the existing highway vehicle monitoring method cannot set matching image processing parameters according to actual road conditions in vehicle diversion sections, resulting in poor monitoring effect when dealing with complex traffic conditions, affecting vehicle diversion safety and traffic efficiency.

[0005] In view of the above problems, the present invention provides a method and system for enhancing highway monitoring images.

[0006] In a first aspect, the present invention provides a method for enhancing highway monitoring images, which is implemented through a highway monitoring image enhancement system, including: connecting a full-structure camera, setting an initial highway partition, wherein the initial highway partition is based on the highway lanes in a target area as a division condition; within the target area, adding lane change marking points, wherein the positions corresponding to the lane change marking points are mapped one-to-one with the entrance and exit ramps and service areas of the target area; based on the lane change marking points, traffic flow management is performed according to the initial highway partition, and traffic flow management coordinates are set, wherein the traffic flow management coordinates are marked as divergence points and merging points; introducing vehicle type information, setting vehicle initial Partitioning, the initial vehicle partitioning is based on the type and size of vehicles in the target area; in the target area, road marking segments are added, and the positions corresponding to the road marking segments are mapped one by one to the uphill and downhill sections and tunnel sections of the target area; on the basis of the road marking segments, traffic risk management is performed according to the initial vehicle partitioning, and traffic risk management sections are set, and the traffic risk management sections are marked as avoidance control sections and speed limit reminder sections; according to the traffic flow management coordinates and the initial highway partitioning, the traffic risk management sections and the initial vehicle partitioning, image processing parameters are matched, and the monitoring image enhancement processing of the full-structure camera is performed.

[0007] In a second aspect, the present invention further provides a highway monitoring image enhancement system for executing a highway monitoring image enhancement method as described in the first aspect, including: a highway initial partition setting module, used to connect a full-structure camera to set the initial highway partition, the initial highway partition is based on the highway lanes in the target area as the division condition; a lane change mark point adding module, used to add lane change mark points in the target area, the positions corresponding to the lane change mark points are mapped one by one with the entrance and exit ramps and service areas of the target area; a traffic management coordinate setting module, used to perform traffic flow management based on the lane change mark points and according to the initial highway partition, and set traffic flow management coordinates, the traffic flow management coordinates are marked as divergence points and merging points; a vehicle initial partition setting module, used to introduce vehicles The module comprises a module for adding a road marking segment, a module for adding a road marking segment in the target area, and a corresponding position of the road marking segment is mapped one-to-one with the uphill and downhill sections and the tunnel sections in the target area; a module for setting a risk management section, a module for performing traffic risk management based on the road marking segment and the initial vehicle partition, and setting a traffic risk management section, and the traffic risk management section is marked as an avoidance control section and a speed limit reminder section; and an image processing parameter matching module, a module for matching image processing parameters according to the traffic flow management coordinates and the initial highway partition, the traffic risk management section and the initial vehicle partition, and performing monitoring image enhancement processing of the full-structure camera.

[0008] One or more technical solutions provided in the present invention have at least the following technical effects or advantages:

[0009] By adding lane change marking points in the target area, and based on the lane change marking points, traffic flow management is performed according to the initial partition of the highway, and traffic flow management coordinates are set, and the traffic flow management coordinates are marked as diversion points and merging points; on the other hand, road surface marking segments are added in the target area, and based on the road surface marking segments, traffic risk management is performed according to the initial partition of the vehicle, and traffic risk management sections are set, and the traffic risk management sections are marked as avoidance control sections and speed limit reminder sections; finally, according to the traffic flow management coordinates and the initial partition of the highway, the traffic risk management sections and the initial partition of the vehicle, the image processing parameters are matched, and the monitoring image enhancement processing of the full-structure camera is performed; the adaptability of the monitoring image processing parameters to the diversion road conditions can be improved, the image monitoring quality of the diversion sections can be ensured, the technical goal of accurate vehicle monitoring under complex traffic conditions can be achieved, and the technical effect of improving vehicle diversion safety and traffic efficiency can be achieved.

[0010] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0012] Figure 1 A schematic diagram of a process of enhancing a highway monitoring image according to the present invention;

[0013] Figure 2 The present invention is a schematic structural diagram of a highway monitoring image enhancement system.

[0014] Description of reference numerals:

[0015] A highway initial partition setting module 11, a lane change mark point adding module 12, a traffic management coordinate setting module 13, a vehicle initial partition setting module 14, a road surface mark segment adding module 15, a risk management segment setting module 16, and an image processing parameter matching module 17. DETAILED DESCRIPTION

[0016] The present invention provides a method and system for enhancing highway monitoring images, which solves the technical problem that the existing highway vehicle monitoring methods cannot set matching image processing parameters according to actual road conditions in vehicle diversion sections, resulting in poor monitoring effects when dealing with complex traffic conditions, affecting vehicle diversion safety and traffic efficiency. The adaptability of monitoring image processing parameters to diversion road conditions can be improved, the image monitoring quality of diversion sections can be ensured, the technical goal of accurate vehicle monitoring under complex traffic conditions can be achieved, and the technical effect of improving vehicle diversion safety and traffic efficiency can be achieved.

[0017] Below, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0018] For example, please refer to the attached Figure 1 The present invention provides a highway monitoring image enhancement method, which is applied to a highway monitoring image enhancement system and specifically includes the following steps:

[0019] Step 1: Connect the full-structure camera and set the initial highway partition, which is based on the highway lanes in the target area.

[0020] Specifically, the full-structure camera is a camera device with a wide viewing angle and all-round capture capabilities. It can fully monitor the target area and provide high-definition, panoramic image data. It is widely used in traffic management systems. First, the full-structure camera is used to fully scan the target area. According to the specific distribution of highway lanes, that is, according to the geometric layout of the lanes, vehicle flow and driving direction, etc., the lanes are divided to ensure that the monitoring coverage of each lane is accurate and uniform, and different initial monitoring areas are divided and set as the initial partition of the highway. Through the reasonable division of the initial partition of the highway, the monitoring system can achieve efficient monitoring on each lane, providing basic data support for subsequent traffic management and image processing.

[0021] Step 2: Add lane change marking points in the target area, and the positions corresponding to the lane change marking points are mapped one by one with the entrance and exit ramps and service areas of the target area.

[0022] Specifically, when monitoring the highway in the target area, first, add multiple lane change marking points. The selection of these marking points is based on the actual location of the ramp entrances and exits and the service area entrances and exits for precise mapping, and there is a one-to-one correspondence with the entrance and exit ramps and service areas in the target area. By setting these lane change marking points, the system can monitor the lane change behavior of vehicles in the target area in real time, analyze the frequency and path of vehicle lane changes, and better predict and manage traffic distribution. These lane change marking points can also assist the traffic management system in optimizing traffic flow control measures, such as guiding vehicles to merge or divert, or warning of potential traffic hazards (such as frequent lane changes or speeding), ultimately improving safety and traffic efficiency in the diversion area.

[0023] Step 3: Based on the lane change mark points, traffic flow management is performed according to the initial partitioning of the highway, and traffic flow management coordinates are set. The traffic flow management coordinates are marked as diverging points and merging points.

[0024] Specifically, firstly, relying on the information of the initial division of the highway and the lane change marking points, the traffic flow in the target area is monitored and managed in real time. Combined with the lane change behavior of the vehicle, the system can accurately identify the vehicle's flow pattern and path selection. Then, based on the lane change marking points, traffic flow management coordinates are set for key locations. These coordinate points are mainly marked as diversion points and confluence points. The diversion point refers to the diversion position of the vehicle from the main road to the ramp or other roads; and the confluence point is the key position where the vehicle merges from the ramp or branch road into the main road. By setting the traffic flow management coordinates, the system can effectively monitor and manage traffic flow changes, avoid traffic congestion, and improve the safe passing rate and traffic efficiency of vehicles.

[0025] Step 4: Introduce vehicle type information and set the initial vehicle partition, where the initial vehicle partition is based on the vehicle type and size within the target area.

[0026] Specifically, the type information of various types of vehicles entering the target area is obtained and analyzed, including but not limited to small cars, large trucks, buses, etc., where the type information usually includes the length, width, height and other size data of the vehicle. Then, based on the vehicle type and size data, the target area is divided into multiple vehicle initial partitions. These partitions are divided according to the size characteristics of the vehicle to ensure that different types of vehicles can travel or stay in suitable partitions. For example, large trucks and small vehicles will be assigned to different lanes or areas to avoid poor traffic flow or safety hazards caused by differences in vehicle size. By setting the initial vehicle partitions, the driving paths and behaviors of different types of vehicles can be managed more accurately, preventing traffic congestion or safety problems caused by vehicle size differences, and further improving the intelligence and accuracy of traffic management.

[0027] Step 5: Add road marking segments in the target area, and the positions corresponding to the road marking segments are mapped one by one with the uphill and downhill sections and tunnel sections of the target area.

[0028] Specifically, the road conditions in the target area are identified and marked by a full-structure camera, especially special sections such as uphill and downhill sections and tunnel sections, and then corresponding road marking sections are set, wherein the corresponding positions of the road marking sections are mapped one by one with the uphill and downhill sections and tunnel sections of the target area, that is, each uphill and downhill section or tunnel section corresponds to a road marking section, and the positions and ranges of these road marking sections are accurately mapped with the actual uphill and downhill sections and tunnel sections to ensure that the system can monitor regional changes in real time. The setting of the road marking section can include information such as slope, road surface material, and lighting conditions, and is linked with the vehicle monitoring system. By setting the road marking section, the system can optimize the management of the unique traffic conditions of special sections such as uphill and downhill sections and tunnels, which helps to improve the accuracy of the monitoring system, ensure the safe passage of vehicles in these complex sections, and further improve the overall traffic efficiency.

[0029] Step 6: Based on the road marking segment, traffic risk management is performed according to the initial vehicle partition, and traffic risk management sections are set. The traffic risk management sections are marked as avoidance control sections and speed limit reminder sections.

[0030] Specifically, since large trucks are heavy, have poor flexibility, and have long braking distances in emergency situations, in order to improve traffic safety, avoidance control sections and speed limit reminder sections are added to specific sections of the road. For example, the avoidance control section is set in sections where large trucks pass frequently, and the distance between small vehicles and large trucks is adjusted by monitoring the traffic dynamics; the speed limit reminder section is set in sections where danger is likely to occur, such as downhill slopes, sharp bends, etc., and a speed limit reminder is issued before the large truck enters these areas through dynamic speed limit signs or on-board reminder systems. First, the information in the road marking segment and the data of the initial vehicle partition are combined to comprehensively analyze the potential traffic risks in the target area, including safety hazards caused by factors such as changes in road slope, insufficient lighting in the tunnel, and mismatch of vehicle types and sizes. Then, based on the risk analysis results, specific sections are marked as traffic risk management sections, where traffic risk management sections include avoidance control sections and speed limit reminder sections. For example, on uphill and downhill sections, the system may mark them as "avoidance control sections" to remind drivers to maintain appropriate vehicle distance and drive carefully in these sections to avoid collision accidents caused by improper speed or emergency lane changes. In tunnel sections or other sections with limited vision, the system may mark them as "speed limit reminder sections" to remind drivers to slow down through speed limit signs or dynamic information displays to reduce accident risks. At the same time, vehicles of different types and sizes have different corresponding avoidance control sections and speed limit reminder sections.

[0031] By setting up traffic risk management sections, we can effectively prevent and control possible traffic accidents and improve the driving safety of vehicles on complex roads. It also helps to optimize traffic flow and avoid congestion or emergencies caused by improper driving behavior, thereby improving the traffic efficiency and safety level of the entire road.

[0032] Step 7: Match image processing parameters according to the traffic flow management coordinates and the initial highway partition, the traffic risk management section and the initial vehicle partition, and perform monitoring image enhancement processing of the full-structure camera.

[0033] Specifically, according to the information of the pre-set traffic flow management coordinates (divergence points and confluence points) and the initial partition of the highway, as well as the traffic risk management sections (avoidance control sections and speed limit reminder sections) and the initial partition of the vehicle, the image processing parameters of the full-structure camera are dynamically adjusted to ensure that the image monitoring effect meets the expected monitoring requirements, wherein the image processing parameters include the frame rate, which is a key parameter affecting the quality and real-time performance of the monitoring video, that is, the frame rate of the camera is dynamically adjusted according to actual needs. For example, at key traffic nodes such as divergence points and confluence points, the frame rate of the camera can be increased to capture the subtle dynamic changes of the vehicle, ensure the continuity and real-time performance of the monitoring screen, reduce the screen freeze or information loss caused by the low frame rate, and thus more accurately track and analyze the vehicle's driving trajectory. Finally, the monitoring image enhancement processing of the full-structure camera is performed according to the image processing parameters to ensure that clear and accurate monitoring images can still be provided under complex traffic conditions, thereby realizing accurate monitoring of the highway and improving the safety and efficiency of vehicle diversion and passage.

[0034] The highway monitoring image enhancement method is applied to a highway monitoring image enhancement system, which can solve the technical problem that the existing highway vehicle monitoring method cannot set matching image processing parameters according to actual road conditions in vehicle diversion sections, resulting in poor monitoring effect when dealing with complex traffic conditions, affecting vehicle diversion safety and traffic efficiency. By adding lane change marking points in the target area, and based on the lane change marking points, traffic flow management is performed according to the initial partition of the highway, and traffic flow management coordinates are set, and the traffic flow management coordinates are marked as diversion points and merging points; on the other hand, road surface marking segments are added in the target area, and based on the road surface marking segments, traffic risk management is performed according to the initial partition of the vehicle, and traffic risk management sections are set, and the traffic risk management sections are marked as avoidance control sections and speed limit reminder sections; finally, according to the traffic flow management coordinates and the initial partition of the highway, the traffic risk management sections and the initial partition of the vehicle, the image processing parameters are matched, and the monitoring image enhancement processing of the full-structure camera is performed; the adaptability of the monitoring image processing parameters to the diversion road conditions can be improved, the image monitoring quality of the diversion sections can be ensured, the technical goal of accurate vehicle monitoring under complex traffic conditions can be achieved, and the technical effect of improving vehicle diversion safety and traffic efficiency can be achieved.

[0035] Further, according to the traffic flow management coordinates and the initial highway partition, the traffic risk management section and the initial vehicle partition, image processing parameters are matched. Before that, step seven of the present invention includes:

[0036] By comparing the traffic flow management coordinates with the initial highway partition and the accident instance in the target area, a first safety goal is added and a first feedback loop is established, wherein the first safety goal is a safety index corresponding to the road congestion point; by comparing the traffic risk management section with the initial vehicle partition and the accident instance in the target area, a second safety goal is added and a second feedback loop is established, wherein the second safety goal is a safety index corresponding to the road risk mark; the first feedback loop is combined with the second feedback loop to optimize and update the initial highway partition and the initial vehicle partition.

[0037] Specifically, firstly, the traffic flow management coordinates and the initial partition of the highway are used to conduct risk analysis on the traffic accidents that may occur in the target area, and the relevant historical accident instances of the target area are called. By analyzing the accident instances that occurred in these areas in the past, the system can identify potential road congestion points and other high-risk areas. Then, on this basis, the first safety goal corresponding to these congestion points is set. The first safety goal is the safety index corresponding to the road congestion point, which can be set according to the actual scenario, including the maximum speed, the minimum distance between vehicles, the maximum vehicle throughput and other indicators. Then, based on the first safety goal, a first feedback loop is established. The first feedback loop refers to the first decision loop established by comparing the accident instances in the target area, including monitoring and analyzing real-time traffic data, evaluating the achievement of the first safety goal, and dynamically adjusting traffic management measures. For example, the surveillance camera captures real-time traffic flow and accident data, and by comparing the first safety goal, identifies the current potential risk points, and automatically triggers the predetermined management strategy to prevent accidents. This feedback loop mechanism helps to improve the safety and traffic efficiency of vehicle diversion at key nodes and high-risk areas of highways and reduce the incidence of traffic accidents.

[0038] On the other hand, by using the initial partition of traffic risk management sections and vehicles, calling historical accident examples related to the target area, a comprehensive risk analysis is conducted on the high-risk sections that may exist in the target area, and potential risk factors of specific sections are identified, such as high-incidence points of accidents on uphill and downhill sections, rear-end collisions caused by light changes at tunnel entrances, etc. Based on this analysis, a second safety goal is set. The second safety goal is a safety indicator set according to specific road risk markers, which can be personalized according to actual scenarios and historical data, such as lane change restrictions, minimum safe vehicle distance, etc. A second feedback loop is further established based on the second safety goal. The second feedback loop refers to a decision-making loop for high-risk sections established based on the accident examples in the target area. It includes monitoring and analyzing real-time traffic data, evaluating the achievement of the second safety goal, and dynamically adjusting traffic management measures. Through the second feedback loop, the system can continuously evaluate and dynamically adjust management measures in the identified high-risk sections to ensure that the traffic safety indicators in these areas always meet the second safety goal. This mechanism can not only significantly reduce the accident rate, but also improve the traffic circulation efficiency in high-risk areas, and ensure the overall safety and smooth operation of the highway.

[0039] Finally, the first feedback loop is combined with the second feedback loop to establish a dual-loop linkage mechanism, that is, the first feedback loop is combined with the second feedback loop to achieve dynamic management and optimization update of the initial partitioning of roads and vehicles through collaborative work. This mechanism utilizes data sharing and linkage decision-making of the two feedback loops to make traffic flow management and risk control complement each other.

[0040] Furthermore, the present invention further comprises the following steps:

[0041] In the first feedback loop, the physical conflict between the divergence jump-out scheduling and the merging jump-out scheduling in the target area is identified; at the same time, traffic flow overload is identified to determine the road congestion point.

[0042] Specifically, in the first feedback loop, the physical conflict between the divergence jump-out scheduling and the merging jump-out scheduling in the target area is identified. The divergence jump-out scheduling refers to the vehicle entering the ramp or branch road from the main road, and the merging jump-out scheduling refers to the vehicle merging from the ramp or branch road into the main road. In the process of traffic management in the target area, the divergence jump-out scheduling and the merging jump-out scheduling may occur at the same time, especially in the area close to the divergence or merging point. Through the traffic flow management coordinates, real-time traffic data and the information of the initial partition of the highway, the physical conflicts of these divergence and merging scheduling are identified, wherein the physical conflicts include conflicts caused by lane staggering and queue conflicts caused by excessive traffic flow. At the same time, traffic flow overload identification is performed, that is, the real-time traffic flow monitoring function in the first feedback loop is used to continuously collect data such as vehicle density and speed on the road, and compare them with the preset flow threshold. Once the traffic flow in a certain area is detected to exceed the set threshold, it is determined that the area has a traffic flow overload phenomenon, and the road area where congestion occurs is determined and marked as a road congestion point. Through the above steps, the system can effectively identify and resolve the physical conflict between diversion jump-out scheduling and merging jump-out scheduling, and at the same time identify traffic flow overload, ensure real-time monitoring and management of road congestion points, and improve the traffic efficiency and driving safety of highways.

[0043] Furthermore, the present invention further comprises the following steps:

[0044] In the second feedback loop, the logical conflict between the lane avoidance and lane change scheduling and the speed limit adjustment scheduling in the target area is identified; at the same time, a traffic risk classification assessment is performed to determine the road risk marker.

[0045] Specifically, in the second feedback loop, the logical conflict between the lane change avoidance scheduling and the speed limit adjustment scheduling in the target area is identified, where the lane change avoidance scheduling means that in order to avoid obstacles, accidents or congestion ahead, the system instructs the vehicle to perform lane change scheduling in certain specific areas, guides the vehicle to switch from one lane to another lane, and avoids potential collision risks; speed limit adjustment scheduling means that in order to deal with traffic risks in specific sections, the system dynamically adjusts the vehicle speed according to information such as road conditions and traffic flow, and limits the speed to ensure driving safety. By real-time monitoring of the vehicle's lane change and speed limit scheduling status, the logical conflict between the two is analyzed. For example, if the vehicle frequently changes lanes in the speed limit area and cannot maintain a stable speed, the system will identify this logical conflict. At the same time, a traffic risk classification assessment is performed, and traffic risks are assessed based on historical accident instances, real-time monitoring data and current traffic conditions. According to the assessment results, traffic risks are divided into multiple levels (such as low risk, medium risk, high risk, etc.), and each risk level area is marked in the system for subsequent management and scheduling decisions to determine road risk markers. Through the above steps, the second feedback loop can effectively identify the logical conflict between avoidance lane change scheduling and speed limit adjustment scheduling, and through traffic risk classification assessment and road risk marking, ensure that the system can dynamically adjust the scheduling plan to achieve more accurate traffic management.

[0046] Furthermore, the first feedback loop is combined with the second feedback loop to optimize and update the initial road partition and the initial vehicle partition. The present invention further includes the following steps:

[0047] The initial highway partition is used as the starting point of the first feedback loop, and the first feedback loop is initialized and configured, and the configuration index of the first feedback loop is associated and mapped with the road congestion point; the initial vehicle partition is used as the starting point of the second feedback loop, and the second feedback loop is initialized and configured, and the configuration index of the second feedback loop is associated and mapped with the road risk mark; after the initialization configuration of the first feedback loop and the second feedback loop, the first feedback loop is combined with the second feedback loop to establish a dual-loop linkage mechanism, and the initial highway partition and the initial vehicle partition are optimized and updated.

[0048] Specifically, the initial partition of the highway is used as the starting point of the first feedback loop, and the first feedback loop is initialized and configured. That is, when the first feedback loop starts, the initial partition of the highway is first used as its starting point, which means that the system will preliminarily partition the highway in the target area according to the road structure, the number of lanes, the driving direction, etc. On this basis, according to the historical traffic flow data, accident instances and the current real-time monitoring, the key indicators associated with the road congestion points are configured, wherein the configuration indicators of the first feedback loop are associated with the road congestion points and mapped. For example, according to the congestion conditions of different road sections, the system sets a reasonable maximum speed for each initial partition of the highway; according to the traffic density of the congestion point, a safe minimum vehicle distance is set to ensure the safe passage of vehicles in the high-traffic area; a flow limit is set for each congestion point, and when it is exceeded, the system triggers an early warning or adjusts the flow distribution, etc.

[0049] On the other hand, the initial vehicle partition is used as the starting point of the second feedback loop, and the second feedback loop is initialized and configured, that is, in the second feedback loop, the system uses the initial vehicle partition as its starting point, and the initial vehicle partition divides the vehicle based on vehicle type, size and performance. Different types of vehicles (such as cars, trucks, buses, etc.) have different partitioning strategies. During the configuration process, the system sets configuration indicators associated with road risk markers based on historical accident data and risk assessment, wherein the configuration indicators of the second feedback loop are associated with the road risk markers and mapped. For example, for high-risk areas, such as sharp bends or tunnel sections, the system will set different speed limit rules for different types of vehicles to avoid accidents caused by differences in vehicle characteristics; in high-risk sections, the system can set lane restrictions for certain vehicle types to prevent oversized or overweight vehicles from entering unsuitable sections, etc.

[0050] Finally, after the initial configuration of the first feedback loop and the second feedback loop, the first feedback loop is combined with the second feedback loop to establish a dual-loop linkage mechanism. The goal of this linkage mechanism is to ensure the coordinated optimization of the initial partition of the highway and the initial partition of the vehicle, including real-time data sharing, linkage response mechanism, dynamic adjustment strategy, etc. For example, under the dual-loop linkage mechanism, dynamic adjustment is performed based on the real-time data of the initial partition of the highway and the initial partition of the vehicle. If the traffic in a certain area suddenly increases or the risk increases, the system can instantly optimize the partition configuration, adjust the speed limit, lane use and other measures to ensure the smooth and safe traffic flow. Finally, with the support of the dual-loop linkage mechanism, the initial partition of the highway and the initial partition of the vehicle are monitored and adjusted in real time, and the system continuously optimizes the partition scheme based on the data in the feedback loop. By building a dual-loop linkage mechanism, it is not only possible to respond to real-time changes in traffic flow, but also to dynamically adjust the vehicle scheduling plan according to the risk level, thereby greatly improving the accuracy, efficiency and safety of traffic management.

[0051] Further, according to the traffic flow management coordinates and the initial highway partition, the traffic risk management section and the initial vehicle partition, image processing parameters are matched, and step seven of the present invention includes:

[0052] Optimize and update the update intensity of the first feedback loop under the dual-loop linkage mechanism: ,in, and is the adjustment parameter, represents the traffic flow density; optimizing and updating the update intensity of the second feedback loop under the dual-loop linkage mechanism: ,in, and is the adjustment parameter, Indicates the risk rate of a certain road section; based on the first feedback loop and the second feedback loop, matches the image processing parameters, the image processing parameters including the frame rate.

[0053] Specifically, the update intensity of the first feedback loop under the dual-loop linkage mechanism is optimized and updated using the first intensity calculation function, wherein the expression of the first intensity calculation function is: , in the first intensity calculation function, is the first update intensity, and is the adjustment parameter, The second intensity calculation function is used to optimize and update the update intensity of the second feedback loop under the dual-loop linkage mechanism, wherein the expression of the second intensity calculation function is: , in the second intensity calculation function, is the second update strength, and is the adjustment parameter, Indicates the risk rate of a certain road section. Finally, matching image processing parameters are set based on the first feedback loop and the second feedback loop. The image processing parameters include frame rate. For example, when the first feedback loop or the second feedback loop detects traffic congestion, excessive traffic density, or potential accident risk in a certain area, the system will automatically increase the image frame rate of the surveillance camera. By increasing the frame rate, the subtle movements of the vehicle and changes on the road can be captured more quickly, thereby improving the system's response speed to traffic flow and risks.

[0054] By dynamically setting image processing parameters based on the real-time traffic flow and risk information required by the first and second feedback loops, it can be ensured that the monitoring system can respond to traffic changes in a timely manner and capture accurate image data, thereby providing strong support for traffic management and accident prevention. This dynamic adjustment mechanism improves the intelligence level of the system and effectively optimizes resource utilization efficiency.

[0055] Furthermore, based on the first feedback loop and the second feedback loop, the image processing parameters are matched, and the present invention further includes the following steps:

[0056] Evaluate the effect of surveillance image enhancement processing:

[0057] ,in, Indicates the effect of monitoring image enhancement processing, represents the enhanced surveillance image intensity, represents the original surveillance image intensity, is the weight coefficient for monitoring image quality, Indicates the target frame rate, Indicates the current frame rate. is the frame rate weighting factor.

[0058] Specifically, the image enhancement effect evaluation function is: , in the image enhancement effect evaluation function, Indicates the effect of monitoring image enhancement processing, represents the enhanced surveillance image intensity, represents the original surveillance image intensity, is the weight coefficient for monitoring image quality, Indicates the target frame rate, Indicates the current frame rate. is the weight coefficient of the frame rate. The image enhancement effect evaluation function is further used to set matching image processing parameters based on the first feedback loop and the second feedback loop.

[0059] In summary, the highway monitoring image enhancement method provided by the present invention has the following technical effects:

[0060] By adding lane change marking points in the target area, and based on the lane change marking points, traffic flow management is performed according to the initial partition of the highway, and traffic flow management coordinates are set, and the traffic flow management coordinates are marked as diversion points and merging points; on the other hand, road surface marking segments are added in the target area, and based on the road surface marking segments, traffic risk management is performed according to the initial partition of the vehicle, and traffic risk management sections are set, and the traffic risk management sections are marked as avoidance control sections and speed limit reminder sections; finally, according to the traffic flow management coordinates and the initial partition of the highway, the traffic risk management sections and the initial partition of the vehicle, the image processing parameters are matched, and the monitoring image enhancement processing of the full-structure camera is performed; the adaptability of the monitoring image processing parameters to the diversion road conditions can be improved, the image monitoring quality of the diversion sections can be ensured, the technical goal of accurate vehicle monitoring under complex traffic conditions can be achieved, and the technical effect of improving vehicle diversion safety and traffic efficiency can be achieved.

[0061] Embodiment 2: Based on the same inventive concept as the highway monitoring image enhancement method in the above embodiment, the present invention also provides a highway monitoring image enhancement system, see the attached Figure 2 ,include:

[0062] The highway initial partition setting module 11 is used to connect the full-structure camera and set the initial partition of the highway. The initial partition of the highway is divided based on the highway lanes in the target area; the lane change mark point adding module 12 is used to add lane change mark points in the target area. The positions corresponding to the lane change mark points are mapped one by one with the entrance and exit ramps and service areas of the target area; the traffic flow management coordinate setting module 13 is used to perform traffic flow management based on the lane change mark points and according to the initial partition of the highway, and set the traffic flow management coordinates. The traffic flow management coordinates are marked as divergence points and merging points; the vehicle initial partition setting module 14 is used to introduce vehicle type information and set the vehicle initial partition. The vehicle initial partition is based on the target area. The size of the vehicle type within is used as the division condition; a road surface marking segment adding module 15 is used to add a road surface marking segment in the target area, and the corresponding position of the road surface marking segment is mapped one by one with the uphill and downhill sections and tunnel sections of the target area; a risk management section setting module 16 is used to perform traffic risk management and set traffic risk management sections based on the road surface marking segment and the initial vehicle partition, and the traffic risk management sections are marked as avoidance control sections and speed limit reminder sections; an image processing parameter matching module 17 is used to match image processing parameters according to the traffic flow management coordinates and the initial highway partition, the traffic risk management section and the initial vehicle partition, and perform monitoring image enhancement processing of the full-structure camera.

[0063] Furthermore, the highway monitoring image enhancement system is also used for:

[0064] By comparing the traffic flow management coordinates with the initial highway partition and the accident instance in the target area, a first safety goal is added and a first feedback loop is established, wherein the first safety goal is a safety index corresponding to the road congestion point; by comparing the traffic risk management section with the initial vehicle partition and the accident instance in the target area, a second safety goal is added and a second feedback loop is established, wherein the second safety goal is a safety index corresponding to the road risk mark; the first feedback loop is combined with the second feedback loop to optimize and update the initial highway partition and the initial vehicle partition.

[0065] Furthermore, the highway monitoring image enhancement system is also used for:

[0066] In the first feedback loop, the physical conflict between the divergence jump-out scheduling and the merging jump-out scheduling in the target area is identified; at the same time, traffic flow overload is identified to determine the road congestion point.

[0067] Furthermore, the highway monitoring image enhancement system is also used for:

[0068] In the second feedback loop, the logical conflict between the lane avoidance and lane change scheduling and the speed limit adjustment scheduling in the target area is identified; at the same time, a traffic risk classification assessment is performed to determine the road risk marker.

[0069] Furthermore, the highway monitoring image enhancement system is also used for:

[0070] The initial highway partition is used as the starting point of the first feedback loop, and the first feedback loop is initialized and configured, and the configuration index of the first feedback loop is associated and mapped with the road congestion point; the initial vehicle partition is used as the starting point of the second feedback loop, and the second feedback loop is initialized and configured, and the configuration index of the second feedback loop is associated and mapped with the road risk mark; after the initialization configuration of the first feedback loop and the second feedback loop, the first feedback loop is combined with the second feedback loop to establish a dual-loop linkage mechanism, and the initial highway partition and the initial vehicle partition are optimized and updated.

[0071] Furthermore, the highway monitoring image enhancement system is also used for:

[0072] Optimize and update the update intensity of the first feedback loop under the dual-loop linkage mechanism: ,in, and is the adjustment parameter, represents the traffic flow density; optimizing and updating the update intensity of the second feedback loop under the dual-loop linkage mechanism: ,in, and is the adjustment parameter, Indicates the risk rate of a certain road section; based on the first feedback loop and the second feedback loop, matches the image processing parameters, the image processing parameters including the frame rate.

[0073] Furthermore, the highway monitoring image enhancement system is also used for:

[0074] Evaluate the effect of surveillance image enhancement processing:

[0075] ,in, Indicates the effect of monitoring image enhancement processing, represents the enhanced surveillance image intensity, represents the original surveillance image intensity, is the weight coefficient for monitoring image quality, Indicates the target frame rate, Indicates the current frame rate. is the frame rate weighting factor.

[0076] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The highway monitoring image enhancement method and specific examples in the aforementioned embodiment 1 are also applicable to a highway monitoring image enhancement system in this embodiment. Through the aforementioned detailed description of a highway monitoring image enhancement method, those skilled in the art can clearly understand a highway monitoring image enhancement system in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0077] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0078] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A highway monitoring image enhancement method, characterized in that: Methods include: Connecting the full-structure camera, setting the initial highway partition, wherein the initial highway partition is based on the highway lanes in the target area; In the target area, a lane change mark point is added, and the position corresponding to the lane change mark point is mapped one by one with the entrance and exit ramps and service areas of the target area; Based on the lane change mark points, traffic flow management is performed according to the initial partition of the highway, and traffic flow management coordinates are set, and the traffic flow management coordinates are marked as diverging points and merging points; Introducing vehicle type information and setting initial vehicle partitions, wherein the initial vehicle partitions are based on the vehicle type and size within the target area as a partition condition; Adding road marking segments in the target area, wherein positions corresponding to the road marking segments are mapped one-to-one with uphill and downhill road segments and tunnel segments in the target area; Based on the road marking segment, traffic risk management is performed according to the initial vehicle partition, and a traffic risk management section is set, wherein the traffic risk management section is marked as an avoidance control section and a speed limit reminder section; Matching image processing parameters according to the traffic flow management coordinates and the initial highway partition, the traffic risk management section and the initial vehicle partition, and performing monitoring image enhancement processing of the full-structure camera; According to the traffic flow management coordinates and the initial highway partition, the traffic risk management section and the initial vehicle partition, the image processing parameters are matched, and the method includes: Optimize and update the update intensity of the first feedback loop under the dual-loop linkage mechanism: Among them, α and β are adjustment parameters, P flow represents the traffic flow density; Optimize and update the update intensity of the second feedback loop under the dual-loop linkage mechanism: Among them, γ and δ are adjustment parameters, R risk Indicates the risk rate of a certain road section; Matching image processing parameters based on the first feedback loop and the second feedback loop, wherein the image processing parameters include a frame rate; Based on the first feedback loop and the second feedback loop, matching image processing parameters, the method includes: Evaluating the effect of surveillance image enhancement processing: E enhance =ω1·∫(I enhanced -I original ) 2 dxdy+ω2·(FPS target -FPS current ) 2 , where E enhance Indicates the effect of monitoring image enhancement processing, I enhanced Represents the enhanced surveillance image intensity, I original represents the original monitoring image intensity, ω1 is the weight coefficient of the monitoring image quality, FPS target Indicates the target frame rate, FPS current represents the current frame rate, and ω2 is the weight coefficient of the frame rate.

2. A highway monitoring image enhancement method as claimed in claim 1, characterized in that: According to the traffic flow management coordinates and the initial highway partition, the traffic risk management section and the initial vehicle partition, matching image processing parameters, before that, the method includes: By comparing the traffic flow management coordinates with the initial highway partition, and comparing the accident instance in the target area, adding a first safety target, and establishing a first feedback loop, wherein the first safety target is a safety index corresponding to the road congestion point; By comparing the traffic risk management section and the initial vehicle partition, a second safety target is added and a second feedback loop is established by comparing the accident instance in the target area, wherein the second safety target is a safety indicator corresponding to the road risk mark; The first feedback loop is combined with the second feedback loop to optimize and update the initial highway partition and the initial vehicle partition.

3. A highway monitoring image enhancement method as claimed in claim 2, characterized in that: In the first feedback loop, physical conflicts between diverging jump-out scheduling and merging jump-out scheduling in the target area are identified; At the same time, traffic flow overload is identified and road congestion points are determined.

4. A highway monitoring image enhancement method as claimed in claim 3, characterized in that: In the second feedback loop, a logical conflict between lane change avoidance scheduling and speed limit adjustment scheduling in the target area is identified; At the same time, traffic risk classification assessment is carried out to determine road risk signs.

5. A highway monitoring image enhancement method as claimed in claim 4, characterized in that: The first feedback loop is combined with the second feedback loop to optimize and update the initial road partition and the initial vehicle partition, the method comprising: Taking the initial highway partition as the starting point of the first feedback loop, initializing the configuration of the first feedback loop, and associating and mapping the configuration index of the first feedback loop with the road congestion point; Taking the initial vehicle partition as the starting point of the second feedback loop, initializing the configuration of the second feedback loop, and associating and mapping the configuration index of the second feedback loop with the road risk mark; After the initialization configuration of the first feedback loop and the second feedback loop, the first feedback loop is combined with the second feedback loop to establish a dual-loop linkage mechanism to optimize and update the initial highway partition and the initial vehicle partition.

6. A highway monitoring image enhancement system, characterized in that: The steps for implementing the highway monitoring image enhancement method according to any one of claims 1 to 5 include: A highway initial partition setting module is used to connect to the full-structure camera and set the highway initial partition, wherein the highway initial partition is based on the highway lanes in the target area as the division condition; A lane change mark point adding module is used to add lane change mark points in the target area, and the positions corresponding to the lane change mark points are mapped one by one with the entrance and exit ramps and service areas of the target area; A traffic flow management coordinate setting module, used to perform traffic flow management based on the lane change mark point and according to the initial partition of the highway, and set traffic flow management coordinates, wherein the traffic flow management coordinates are marked as diverging points and merging points; A vehicle initial partition setting module is used to introduce vehicle type information and set vehicle initial partitions, wherein the vehicle initial partitions are divided based on the vehicle type and size within the target area; A road marking segment adding module, used for adding road marking segments in the target area, wherein positions corresponding to the road marking segments are mapped one by one with uphill and downhill road segments and tunnel segments in the target area; A risk management section setting module is used to perform traffic risk management and set traffic risk management sections based on the road surface marking section and the initial vehicle partition, wherein the traffic risk management sections are marked as avoidance control sections and speed limit reminder sections; An image processing parameter matching module, used to match image processing parameters according to the traffic flow management coordinates and the initial partition of the highway, the traffic risk management section and the initial partition of the vehicle, and perform monitoring image enhancement processing of the full-structure camera; Matching image processing parameters according to the traffic flow management coordinates and the initial highway partition, the traffic risk management section and the initial vehicle partition, including: Optimize and update the update intensity of the first feedback loop under the dual-loop linkage mechanism: Among them, α and β are adjustment parameters, P flow represents the traffic flow density; Optimize and update the update intensity of the second feedback loop under the dual-loop linkage mechanism: Among them, γ and δ are adjustment parameters, R risk Indicates the risk rate of a certain road section; Matching image processing parameters based on the first feedback loop and the second feedback loop, wherein the image processing parameters include a frame rate; Matching image processing parameters based on the first feedback loop and the second feedback loop includes: Evaluating the effect of surveillance image enhancement processing: E enhance =ω1·∫(I enhanced -I original ) 2 dxdy+ω2·(FPS target -FPS current ) 2 , where E enhance Indicates the effect of monitoring image enhancement processing, I enhanced Represents the enhanced surveillance image intensity, I original represents the original monitoring image intensity, ω1 is the weight coefficient of the monitoring image quality, FPS target Indicates the target frame rate, FPS current represents the current frame rate, and ω2 is the weight coefficient of the frame rate.

Images