System and method for detecting road safety through image detection algorithm of depth recognition

Abstract

The invention relates to the technical field of road safety detection, and discloses a system and method for detecting road safety through an image detection algorithm of depth recognition, and the method comprises the steps: installing a high-definition camera at a key position of a road, and guaranteeing the complete coverage and no blind area; and comprehensive coverage is ensured. According to the system and the method for detecting the road safety through the image detection algorithm based on depth recognition, in order to enable the system to further improve the accuracy and reliability of road safety monitoring, an image acquisition module is cooperated with a data transmission unit through a camera unit of the image acquisition module; therefore, the effect of providing comprehensive and high-quality image data for road safety detection is achieved, the data transmission unit fuses wired and wireless network technologies, high-definition image data collected by the camera is efficiently and stably transmitted to the image processing and analysis module, and the real-time performance and integrity of the image data are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of road safety detection, and in particular to a road safety detection system and method using an image detection algorithm with depth recognition. Background Art

[0002] The road safety system detects road traffic through deep recognition image detection algorithms, aiming to improve road safety and reduce the incidence of traffic accidents. The system uses deep learning algorithms to monitor and analyze road traffic conditions in real time, accurately identify key elements such as pedestrians, vehicles, and traffic signs, and effectively warn of potential safety hazards.

[0003] The road safety system is based on a deep learning algorithm. Its workflow includes image acquisition, preprocessing, feature extraction, and target detection. The system first collects road traffic images through a camera. After preprocessing, it uses a deep learning model to extract and identify key elements in the image. Its structure includes a deep learning network, an image processing module, and an early warning system. Through high-speed computing and precise recognition, it can achieve real-time monitoring of road safety.

[0004] At present, most image detection algorithms for detecting road safety systems cannot ensure the real-time and integrity of image data, and cannot efficiently and accurately identify various targets and road anomalies. In view of this, we propose an image detection algorithm for detecting road safety systems through deep recognition and a method thereof. Summary of the invention

[0005] The object of the present invention is to provide a road safety detection system and method thereof using an image detection algorithm for depth recognition to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for detecting road safety by using an image detection algorithm for deep recognition comprises the following steps:

[0008] S1. Install high-definition cameras at key locations on the road to ensure full coverage, no blind spots, and to collect road images in real time;

[0009] S2. Configure wired and wireless networks to ensure smooth communication between the camera and the image processing and analysis unit and ensure the stability of data transmission;

[0010] S3. Install image processing software on the image processing and analysis unit, configure the server and communication module, and ensure smooth data transmission and warning release;

[0011] S4, the camera continuously collects road images at a set frame rate, and the collected image data is transmitted to the image processing and analysis unit in real time through the network;

[0012] S5. During the transmission process, data compression technology such as H.264 or H.265 encoding is used to reduce network bandwidth usage and ensure that the image quality is not significantly affected during the compression process;

[0013] S6, decoding the received image, restoring it from the compressed format to the original image, performing pre-processing operations such as cropping and scaling on the image, and unifying the image size;

[0014] S7, input the preprocessed image into the deep learning model for feature extraction and target detection, identify vehicles, pedestrians, traffic signs and other targets, and determine their locations and categories;

[0015] S8, by learning the normal road image features, detecting the area with large differences from the normal features, judging it as a road anomaly, and determining the location and range of the abnormal area;

[0016] S9, transmitting the detected target information and abnormal condition information to the traffic management center through the communication module, and the server integrates and analyzes the received information;

[0017] S10. Generate warning instructions according to preset rules, release warning information through variable information display screens, traffic light control systems and other channels, and push relevant information to the traffic police department's law enforcement system or mobile phone APP.

[0018] A road safety system is detected by an image detection algorithm through deep recognition. A road safety method is designed by using an image detection algorithm through deep recognition. The safety system includes:

[0019] Image acquisition module: The image acquisition module consists of multiple high-definition cameras with high resolution, wide dynamic range and low-light performance, which can adapt to different lighting and weather conditions. They are distributed at key locations on the road and transmit the collected image data to the image processing and analysis unit through wired or wireless networks, providing real-time and clear image data for road safety monitoring. It includes a camera unit and a data transmission unit;

[0020] Image processing and analysis module: Based on a high-performance computing platform, the image processing and analysis module uses deep learning algorithms to preprocess, extract features, and detect targets on collected road images. It can accurately identify vehicles, pedestrians, traffic signs, and abnormal road conditions, and provide real-time and accurate analysis results for road safety monitoring, helping traffic management and decision-making. It includes a preprocessing unit, a deep learning model unit, and an output and storage unit.

[0021] Communication and warning module: The communication and warning module integrates wired and wireless communication technologies, and is responsible for transmitting image processing results to the traffic management center and warning equipment in real time. It can quickly issue road safety warnings based on the detection results, adjust traffic light controls, and push alarm information to management personnel to ensure timely delivery and effective response of road safety information. It includes a data transmission unit, a warning release unit, and an alarm information push unit.

[0022] Preferably, the camera unit in the image acquisition module is the core of the image acquisition module, and is composed of a plurality of high-definition cameras with high resolution, wide dynamic range and low illumination performance. These cameras are precisely arranged at key positions on the road and can clearly capture road scenes under all-weather and all-weather conditions, providing high-quality image data for subsequent image processing and analysis.

[0023] Preferably, the data transmission unit in the image acquisition module is responsible for efficiently and stably transmitting the high-definition image data captured by the camera to the image processing and analysis module. It integrates wired network and wireless network technologies to ensure real-time and reliable data transmission in various environments, providing a solid communication foundation for road safety monitoring.

[0024] Preferably, the preprocessing unit in the image processing and analysis module is the primary link of the image processing and analysis module, and is responsible for normalizing, cropping, scaling, and other processing of the original image to meet the input requirements of the deep learning model. By optimizing the image data, the preprocessing unit lays a solid foundation for subsequent feature extraction and target detection, ensuring the accuracy and reliability of the image analysis results.

[0025] Preferably, the deep learning model unit in the image processing and analysis module is the core of the image processing and analysis module. It adopts advanced target detection architectures such as YOLO and Faster R-CNN, and is specially trained for road safety monitoring tasks. It can efficiently and accurately identify vehicles, pedestrians, traffic signs and road anomalies, and provide real-time intelligent analysis for road safety.

[0026] Preferably, the output and storage unit in the image processing and analysis module is responsible for organizing and outputting the analysis results of the deep learning model, and securely storing these data and processed images in a server or database to facilitate subsequent query, analysis and traffic management decision-making.

[0027] Preferably, the data transmission unit in the communication and warning module is a key component of the communication and warning module. It transmits the output results of the image processing and analysis module to the traffic management center and related warning equipment in real time and accurately through wired communication technologies such as Ethernet, RS485 and wireless communication technologies such as 4G / 5G and Wi-Fi, ensuring the timely delivery of road safety information.

[0028] Preferably, the warning issuing unit in the communication and warning module is responsible for issuing road safety warning information in a timely manner through channels such as variable information display screens and traffic light control systems based on the results of the image processing and analysis module, reminding passing vehicles to pay attention to changes in road conditions and take corresponding measures to effectively prevent the occurrence of traffic accidents and ensure road safety.

[0029] Preferably, the alarm information push unit in the communication and warning module is an important part of the communication and warning module. It can quickly push the detected emergency road safety information to the mobile phone APP of the traffic management department staff, ensuring that the staff can obtain and process the alarm information as soon as possible, respond to road safety incidents in time, and improve traffic management efficiency.

[0030] Compared with the prior art, the present invention provides a road safety system and method for detecting road safety using an image detection algorithm through depth recognition, which has the following beneficial effects:

[0031] 1. The road safety detection system and method using deep recognition image detection algorithm, in order to further improve the accuracy and reliability of road safety monitoring, achieves the effect of providing comprehensive and high-quality image data for road safety detection through the coordinated cooperation of the image acquisition module through its camera unit and the data transmission unit. The data transmission unit integrates wired and wireless network technologies to efficiently and stably transmit the high-definition image data collected by the camera to the image processing and analysis module, ensuring the real-time and integrity of the image data.

[0032] 2. The road safety detection system and method using deep recognition image detection algorithm, in order to further improve the efficiency of road safety management, achieves the goal of deep intelligent analysis of road images and effective data management through the image processing and analysis module with the help of the preprocessing unit, the deep learning model unit and the output and storage unit. The preprocessing unit normalizes, crops, scales and other processes on the original image so that the image meets the input requirements of the deep learning model and optimizes the image data quality. The deep learning model unit adopts an advanced target detection architecture and is specially trained for road safety monitoring tasks. It can efficiently and accurately identify various targets and road anomalies, and provide real-time intelligent analysis for traffic management.

[0033] 3. The road safety detection system and method using deep recognition image detection algorithm, in order to enable the system to further ensure road traffic safety and improve traffic management efficiency, relies on the linkage of the data transmission unit, the warning release unit and the alarm information push unit through the communication and warning module, thereby achieving the effect of timely transmission and effective response of road safety information. The data transmission unit uses a variety of wired and wireless communication technologies to transmit the output results of the image processing and analysis module to the traffic management center and related warning equipment in real time and accurately, ensuring the timeliness and accuracy of information transmission. The warning release unit timely releases road safety warning information through channels such as variable information display screens and traffic light control systems based on the analysis results, reminding passing vehicles to pay attention to changes in road conditions and effectively preventing traffic accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of a road safety detection system using a depth recognition image detection algorithm according to the present invention;

[0035] Figure 2 This is a schematic diagram of the image acquisition module of the system of the present invention;

[0036] Figure 3 This is a schematic diagram of the image processing and analysis module of the system of the present invention;

[0037] Figure 4 This is a schematic diagram of the communication and early warning module of the system of the present invention;

[0038] Figure 5 The present invention is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] See also Figure 1-5 , the present invention provides a technical solution:

[0041] A method for detecting road safety by using an image detection algorithm for deep recognition comprises the following steps:

[0042] S1. Install high-definition cameras at key locations on the road to ensure full coverage without blind spots, to ensure full coverage and real-time collection of road images. Install high-definition cameras at various intersections on urban roads, including crossroads, T-junctions and roundabouts. The installation angles have been precisely calculated and debugged to clearly capture traffic conditions in all directions of the intersections. Whether it is the driving trajectory of vehicles, the dynamics of pedestrians crossing the street or the display status of traffic signs, they can all be fully recorded. On the highway, a group of cameras are set up at a certain distance to comprehensively monitor the main roads, emergency lanes and ramps of the road, which can effectively monitor vehicle speeding, illegal lane changes and traffic accidents. For special sections such as bridges and tunnels, the installation position of the camera fully considers factors such as light changes and field of view obstruction to ensure accurate image collection in these complex environments, providing all-round data support for road safety monitoring;

[0043] S2. Configure wired and wireless networks to ensure smooth communication between the camera and the image processing and analysis unit, and ensure the stability of data transmission. For areas in urban roads that are close and have complete network infrastructure, wired networks are preferred to connect the camera and the image processing and analysis unit. For example, Ethernet cables are used, which have the characteristics of high speed, stability, and strong anti-interference ability, and can ensure the reliable transmission of large amounts of image data. In some remote areas or locations where cameras are temporarily added, wireless network connections, such as 5G networks, are used to take advantage of their high speed and low latency to ensure that even in complex geographical environments, the image data collected by the camera can be transmitted to the processing unit in a timely and stable manner. At the same time, a network redundancy mechanism is set up. When a wired network fails, it automatically switches to a wireless network, and vice versa, thereby ensuring the uninterrupted communication to the greatest extent.

[0044] S3. Install image processing software on the image processing and analysis unit, configure the server and communication module to ensure smooth data transmission and warning release. Use powerful image processing software optimized for road image analysis. The software has multi-threaded processing capabilities and can process image data from multiple cameras at the same time to improve processing efficiency. The server uses a high-performance enterprise-level server equipped with large-capacity memory, high-speed processor and high-performance GPU accelerator card to cope with the huge demand for computing resources of deep learning models. The communication module integrates a variety of communication protocols, including Ethernet, 4G / 5G, Wi-Fi, etc., and can flexibly select the appropriate communication method according to different application scenarios and network conditions to ensure stable communication with external equipment such as traffic management centers, variable information display screens and traffic light control systems, and ensure that warning information can be released in a timely and accurate manner;

[0045] S4. The camera continuously collects road images at the set frame rate, and the collected image data is transmitted to the image processing and analysis unit in real time through the network. The frame rate of the camera can be flexibly set according to the busyness and importance of the road. For example, at a busy intersection in the city center, a higher frame rate is set to more carefully capture the rapid movement changes of vehicles and pedestrians; while on suburban roads with relatively small traffic volume, the frame rate can be appropriately reduced to reduce the amount of data transmission while ensuring effective monitoring. During the image data transmission process, a reliable network transmission protocol, such as TCP / IP protocol, is used to ensure the integrity and sequence of data transmission and prevent image data loss or disorder. At the same time, the transmission link is monitored in real time. Once a network failure or transmission abnormality is found, the fault diagnosis and repair mechanism is immediately started to ensure that data can be continuously and stably transmitted to the image processing and analysis unit;

[0046] S5. During the transmission process, data compression technology is used to reduce network bandwidth usage to ensure that the image quality is not significantly affected during the compression process. When using H.264 encoding, by optimizing encoding parameters, such as adjusting quantization parameters, frame rate control, and GOP structure, a higher compression ratio is achieved while ensuring image quality. For H.265 encoding, full use is made of its more advanced encoding algorithms, such as block-based motion compensation prediction, adaptive loop filtering and other technologies, to further improve compression efficiency. Compared with H.264 encoding, about 30%-50% of network bandwidth can be saved at the same image quality. At the same time, an image quality assessment algorithm is used during the compression process to perform real-time quality detection on the compressed image. When the image quality is found to have dropped to a certain level, the compression parameters are automatically adjusted to ensure that the image quality always meets the requirements of road safety monitoring.

[0047] S6. Decode the received image, restore it from the compressed format to the original image, perform preprocessing operations such as cropping and scaling on the image, unify the image size, and use efficient decoding algorithms, such as hardware accelerated decoding technology, to use the server's GPU or a dedicated decoding chip to quickly decode H.264 or H.265 encoded image data into the original image format. For the cropping operation, according to the input requirements of the deep learning model, remove irrelevant areas in the image, such as the sky captured by the camera, the edges of roadside buildings, etc., and only retain areas containing key information such as roads, vehicles, pedestrians, etc., to improve the efficiency of model processing. The scaling operation uniformly adjusts the cropped image to the size required by the model, for example, uniformly scaling images of different resolutions to 640×640 pixels. In the scaling process, use high-quality scaling algorithms such as bilinear interpolation to reduce image distortion and ensure that the target features in the image are still clearly discernible after scaling.

[0048] S7. Input the preprocessed image into the deep learning model for feature extraction and target detection, identify vehicles, pedestrians, traffic signs and other targets, and determine their location and category. Use advanced deep learning target detection models, such as the improved model based on YOLOv5. This model can more effectively extract target features in the image by introducing a more complex feature extraction network structure, such as the CSPDarknet network. During the model training process, a large amount of road image data labeled with targets such as vehicles, pedestrians, and traffic signs is used for training. By optimizing the loss function and adjusting the model hyperparameters, the accuracy and generalization ability of the model are improved. In the target detection stage, the model extracts multi-scale features from the input image, predicts the bounding box and category probability of the target on feature maps of different scales, and then screens and merges the overlapping bounding boxes through the non-maximum suppression algorithm to finally determine the accurate location and category information of the target, such as accurately identifying different types of vehicles such as cars, trucks, and motorcycles, as well as the location and walking direction of pedestrians, and the specific meaning of various traffic signs;

[0049] S8. By learning the features of normal road images, detect areas with large differences from normal features, judge them as road anomalies, and determine the location and range of the abnormal areas. First, collect a large amount of normal road image data, including images under different weather conditions, different time periods and different road conditions. Use deep learning models to learn the features of these images and build a feature model of normal roads. In the actual monitoring process, compare the real-time collected images with the normal feature model. When it is found that there are obvious feature differences in certain areas of the image, such as large areas of foreign matter accumulation on the road, abnormal road color and texture, it is judged as a road anomaly. The target detection model is used to determine the bounding box coordinates of the abnormal area in the image, thereby determining its location and range, providing accurate information for subsequent early warning and processing;

[0050] S9. The detected target information and abnormal condition information are transmitted to the traffic management center through the communication module. The server integrates and analyzes the received information. The communication module adopts a high-speed and stable network communication method, such as a fiber optic network or a 5G network, to quickly transmit the target detection and road abnormality information to the server of the traffic management center. After receiving the information, the server first verifies the integrity and accuracy of the data, removes possible erroneous data or duplicate data, and then integrates the information from different cameras and different areas. For example, the vehicle flow information detected by multiple cameras on the same road section is summarized to analyze the formation and development trend of traffic congestion; the road abnormality information is combined with the traffic conditions of surrounding roads to evaluate the scope and degree of the impact of abnormal conditions on the overall traffic. Through the comprehensive analysis of these information, a basis is provided for generating accurate and effective early warning instructions;

[0051] S10. Generate warning instructions according to preset rules, release warning information through channels such as variable information display screens and traffic light control systems, and push relevant information to the traffic police department's law enforcement system or mobile phone APP. The preset rules are formulated according to different target detection results and road abnormalities. For example, when it is detected that the vehicle flow on a certain section exceeds the set threshold and the vehicle speed is lower than a certain standard, it is determined to be traffic congestion, and a warning instruction is generated to display the congestion information of the section on the variable information display screen and suggest vehicles to detour; when obstacles are found on the road and seriously affect traffic, an instruction is generated to control the traffic lights to switch to specific warning signals at the intersection where the obstacles are located, and at the same time, the obstacle location and type information is pushed to the traffic police department's law enforcement system and the surrounding traffic police's mobile phone APP so that the traffic police can go to deal with it in time. The warning information is transmitted to the variable information display screen and the traffic light control system through a reliable communication link to ensure that the information can be displayed and executed in a timely and accurate manner, thereby ensuring the effective management and maintenance of road traffic safety.

[0052] A road safety system is detected by an image detection algorithm through deep recognition. A safety system is designed by a road safety method using an image detection algorithm through deep recognition. The safety system includes: an image acquisition module, an image processing and analysis module, and a communication and early warning module.

[0053] In one embodiment of the present invention, the image acquisition module is composed of multiple high-definition cameras. These cameras have high resolution, wide dynamic range and low illumination performance, and can adapt to different lighting and weather conditions. They are distributed at key positions on the road and transmit the collected image data to the image processing and analysis unit through a wired or wireless network to provide real-time and clear image data for road safety monitoring. The camera unit and the data transmission unit are included. The camera unit in the image acquisition module is the core of the image acquisition module and is composed of multiple high-definition cameras with high resolution, wide dynamic range and low illumination performance. These cameras are precisely arranged at key positions on the road and can clearly capture road scenes under all-weather and all-weather conditions, providing high-quality image data for subsequent image processing and analysis. The data transmission unit in the image acquisition module is responsible for efficiently and stably transmitting the high-definition image data captured by the camera to the image processing and analysis module. It integrates wired network and wireless network technologies to ensure real-time and reliable data transmission in various environments, providing a solid communication foundation for road safety monitoring.

[0054] In one embodiment of the present invention, the image processing and analysis module is based on a high-performance computing platform and uses a deep learning algorithm to preprocess, extract features and detect targets on the collected road images. It can accurately identify vehicles, pedestrians, traffic signs and abnormal road conditions, provide real-time and accurate analysis results for road safety monitoring, and assist in traffic management and decision-making. It includes a preprocessing unit, a deep learning model unit and an output and storage unit. The preprocessing unit in the image processing and analysis module is the primary link of the image processing and analysis module, and is responsible for normalizing, cropping, scaling and other processing of the original image to meet the input requirements of the deep learning model. By optimizing the image data, the preprocessing unit lays a solid foundation for subsequent feature extraction and target detection, ensuring the accuracy and reliability of the image analysis results. The deep learning model unit in the image processing and analysis module is the core of the image processing and analysis module, and adopts advanced YOLO, Faster Target detection architectures such as R-CNN are specially trained for road safety monitoring tasks. They can efficiently and accurately identify vehicles, pedestrians, traffic signs and road anomalies, and provide real-time intelligent analysis for road safety. The output and storage unit in the image processing and analysis module is responsible for organizing and outputting the analysis results of the deep learning model, and at the same time safely storing these data and processed images in the server or database to facilitate subsequent query, analysis and traffic management decision-making.

[0055] In one embodiment of the present invention, the communication and warning module integrates wired and wireless communication technologies, and is responsible for transmitting the image processing results to the traffic management center and the warning equipment in real time. It can quickly issue road safety warnings based on the detection results, adjust the signal light control, and push alarm information to management personnel to ensure the timely transmission and effective response of road safety information. It includes a data transmission unit, a warning issuance unit, and an alarm information push unit. The data transmission unit in the communication and warning module is a key component of the communication and warning module. It transmits the output results of the image processing and analysis module to the traffic management center and the warning equipment in real time and accurately through wired communication technologies such as Ethernet, RS485 and wireless communication technologies such as 4G / 5G and Wi-Fi. Relevant early warning equipment ensures the timely transmission of road safety information. The early warning release unit in the communication and early warning module is responsible for timely releasing road safety early warning information through variable information display screens, traffic light control systems and other channels according to the results of the image processing and analysis module, reminding passing vehicles to pay attention to changes in road conditions and take corresponding measures to effectively prevent traffic accidents and ensure road safety. The alarm information push unit in the communication and early warning module is an important part of the communication and early warning module. It can quickly push the detected emergency road safety information to the mobile phone APP of the traffic management department staff, ensuring that the staff can obtain and process the alarm information as soon as possible, respond to road safety incidents in a timely manner, and improve traffic management efficiency.

[0056] The above generally describes the present invention in detail, but it is obvious to a person skilled in the art that some modifications or improvements can be made to the present invention. Therefore, modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A method for detecting road safety using an image detection algorithm using deep recognition, characterized in that: The following steps are involved: S1. Install high-definition cameras at key locations on the road to ensure full coverage, no blind spots, and to collect road images in real time; S2. Configure wired and wireless networks to ensure smooth communication between the camera and the image processing and analysis unit and ensure the stability of data transmission; S3. Install image processing software on the image processing and analysis unit, configure the server and communication module, and ensure smooth data transmission and warning release; S4, the camera continuously collects road images at a set frame rate, and the collected image data is transmitted to the image processing and analysis unit in real time through the network; S5. During the transmission process, data compression technology is used to reduce network bandwidth usage and ensure that the image quality is not significantly affected during the compression process; S6, decoding the received image, restoring it from the compressed format to the original image, performing pre-processing operations such as cropping and scaling on the image, and unifying the image size; S7, input the preprocessed image into the deep learning model for feature extraction and target detection, identify vehicles, pedestrians, traffic signs and other targets, and determine their locations and categories; S8, by learning the normal road image features, detecting the area with large differences from the normal features, judging it as a road anomaly, and determining the location and range of the abnormal area; S9, transmitting the detected target information and abnormal condition information to the traffic management center through the communication module, and the server integrates and analyzes the received information; S10. Generate warning instructions according to preset rules, issue warning information, and push relevant information to the law enforcement system of the traffic police department.

2. A road safety system using an image detection algorithm based on deep recognition, characterized in that A safety system is designed by using a road safety detection method using a deep recognition image detection algorithm as claimed in claim 1, the safety system comprising: Image acquisition module: The image acquisition module consists of multiple high-definition cameras with high resolution, wide dynamic range and low-light performance, which can adapt to different lighting and weather conditions. They are distributed at key locations on the road and transmit the collected image data to the image processing and analysis unit through wired or wireless networks, providing real-time and clear image data for road safety monitoring. It includes a camera unit and a data transmission unit; Image processing and analysis module: Based on a high-performance computing platform, the image processing and analysis module uses deep learning algorithms to preprocess, extract features, and detect targets on collected road images. It can accurately identify vehicles, pedestrians, traffic signs, and abnormal road conditions, and provide real-time and accurate analysis results for road safety monitoring, helping traffic management and decision-making. It includes a preprocessing unit, a deep learning model unit, and an output and storage unit. Communication and warning module: The communication and warning module integrates wired and wireless communication technologies, and is responsible for transmitting image processing results to the traffic management center and warning equipment in real time. It can quickly issue road safety warnings based on the detection results, adjust traffic light controls, and push alarm information to management personnel to ensure timely delivery and effective response of road safety information. It includes a data transmission unit, a warning release unit, and an alarm information push unit.

3. A road safety detection system using an image detection algorithm for depth recognition according to claim 2, characterized in that: The camera unit in the image acquisition module is the core of the image acquisition module. It is composed of multiple high-definition cameras with high resolution, wide dynamic range and low-light performance. These cameras are precisely arranged at key positions on the road and can clearly capture road scenes under all-weather conditions around the clock, providing high-quality image data for subsequent image processing and analysis.

4. A road safety detection system using an image detection algorithm for depth recognition according to claim 2, characterized in that: The data transmission unit in the image acquisition module is responsible for efficiently and stably transmitting the high-definition image data captured by the camera to the image processing and analysis module. It integrates wired network and wireless network technologies to ensure real-time and reliable data transmission in various environments, providing a solid communication foundation for road safety monitoring.

5. The road safety detection system using image detection algorithm through depth recognition according to claim 2, characterized in that: The preprocessing unit in the image processing and analysis module is the primary link of the image processing and analysis module. It is responsible for normalizing, cropping, scaling and other processing of the original image to meet the input requirements of the deep learning model. By optimizing the image data, the preprocessing unit lays a solid foundation for subsequent feature extraction and target detection, ensuring the accuracy and reliability of the image analysis results.

6. The road safety detection system using image detection algorithm through depth recognition according to claim 2, characterized in that: The deep learning model unit in the image processing and analysis module is the core of the image processing and analysis module. It adopts advanced target detection architectures such as YOLO and Faster R-CNN, and is specially trained for road safety monitoring tasks. It can efficiently and accurately identify vehicles, pedestrians, traffic signs and road anomalies, and provide real-time intelligent analysis for road safety.

7. The road safety detection system using image detection algorithm through depth recognition according to claim 2, characterized in that: The output and storage unit in the image processing and analysis module is responsible for organizing and outputting the analysis results of the deep learning model, and securely storing these data and processed images in a server or database to facilitate subsequent query, analysis and traffic management decision-making.

8. The road safety detection system using image detection algorithm through depth recognition according to claim 2, characterized in that: The data transmission unit in the communication and warning module is a key component of the communication and warning module. It transmits the output results of the image processing and analysis module to the traffic management center and related warning equipment in real time and accurately through wired communication technologies such as Ethernet, RS485 and wireless communication technologies such as 4G / 5G and Wi-Fi, ensuring the timely delivery of road safety information.

9. The road safety detection system using image detection algorithm through depth recognition according to claim 2, characterized in that: The warning issuing unit in the communication and warning module is responsible for issuing road safety warning information in a timely manner through channels such as variable information display screens and traffic light control systems based on the results of the image processing and analysis module, reminding passing vehicles to pay attention to changes in road conditions and take corresponding measures to effectively prevent traffic accidents and ensure road safety.

10. The road safety detection system using image detection algorithm through depth recognition according to claim 2, characterized in that: The alarm information push unit in the communication and warning module is an important part of the communication and warning module. It can quickly push the detected emergency road safety information to the mobile phone APP of the traffic management department staff, ensuring that the staff can obtain and process the alarm information as soon as possible, respond to road safety incidents in time, and improve traffic management efficiency.

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