Vehicle-mounted camera shooting management method and system

Abstract

The invention discloses a vehicle-mounted camera shooting management method and system, and particularly relates to the technical field of image communication, and the system comprises an intelligent image collection processing module which is used for synchronously collecting high-quality image data through a plurality of cameras and carrying out the preprocessing of the image data, and a vehicle-mounted camera shooting management module which is used for carrying out the real-time analysis and recognition of the collected image data, and the real-time image analysis and identification module is used for extracting useful information and providing real-time feedback according to an analysis result. Through efficient image acquisition, real-time analysis, data management and intelligent auxiliary driving functions, the driving safety, the driving stability and the data management efficiency are remarkably improved. The system utilizes a multi-camera synchronization technology and a convolutional neural network denoising algorithm to ensure that high-quality images are captured in various environments, and a dynamic range adjustment module optimizes the image quality under different illumination conditions.

Description

Technical Field

[0001] The present invention relates to the field of image communication technology, and in particular to a vehicle-mounted camera management method and system. Background Art

[0002] In-vehicle cameras are cameras installed on cars or other vehicles to record video and sound during driving. In-vehicle cameras can be used to monitor the interior and surrounding environment of the vehicle, help drivers identify responsibility for traffic accidents, protect vehicle safety, and improve driving safety. In-vehicle cameras usually include cameras in different locations such as front cameras, rear cameras, and built-in cameras. They can also have features such as tracking recording, collision sensing, and night vision.

[0003] However, in the prior art, the existing in-vehicle cameras generally inform the driver of the information near the vehicle through video and audio. However, if the owner of the car is not near the car, it is difficult to get the real-time information of the vehicle. For example, if the owner of the car is at home and the car is parked downstairs and a child plays and accidentally slips and falls over the car, the owner of the car cannot get the information in the first time. Therefore, the practicality of such in-vehicle cameras in actual use still needs to be improved. Therefore, we propose a method and system for managing in-vehicle cameras. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a vehicle-mounted camera management method and system.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A vehicle-mounted camera management method and system, comprising an intelligent image acquisition and processing module for synchronously acquiring high-quality image data through multiple cameras and preprocessing the image data, a real-time image analysis and recognition module for real-time analysis and recognition of the acquired image data, extracting useful information, and providing real-time feedback based on the analysis results, a data storage and management module for efficiently storing and managing the acquired image data, and an intelligent assisted driving module for providing the driver with an intelligent assisted driving function using the results of image analysis and recognition; The intelligent image acquisition and processing module includes a multi-camera synchronization module for managing and synchronizing data streams of multiple cameras on the vehicle to ensure that all image data are aligned at the same timestamp, an image denoising module for using a convolutional neural network to reduce noise in the image and improve the clarity and quality of the image, a dynamic range adjustment module for automatically adjusting the dynamic range of the camera under different lighting conditions to ensure that clear images can be captured in strong light or low light environments, and an image compression transmission module for effectively compressing high-resolution images while retaining key details and transmitting them in real time to a central processing unit (CPU) or the cloud for further processing and storage; The real-time image analysis and recognition module includes an object detection module for detecting and identifying various objects in an image in real time using a deep learning model YOLO, a lane departure warning module for detecting lane lines through image processing technology, evaluating whether the vehicle has deviated from the lane, and issuing a warning to the driver in a timely manner, a blind spot monitoring module for monitoring the vehicle's blind spots in real time through side and rear cameras, detecting whether there are other vehicles or pedestrians in the blind spots, and issuing a warning when necessary, and a driving status monitoring module for monitoring the driver's attention and fatigue status through facial recognition and posture analysis technology, and promptly reminding the driver to pay attention to safe driving; The intelligent assisted driving module includes an adaptive cruise module for combining camera and lidar data to detect the speed and distance of the vehicle in front in real time, and realize the functions of automatically adjusting the vehicle speed and maintaining a safe distance; an automatic parking module for combining camera and ultrasonic sensor data to realize the automatic parking function and help the driver park safely in a small parking space; a collision warning avoidance module for using front and rear camera and radar data to evaluate the collision risk in real time and take risk avoidance measures such as automatic braking or steering when necessary; and an intelligent navigation module for integrating high-definition maps and real-time traffic information, identifying road signs and traffic conditions through cameras, and providing intelligent navigation suggestions to the driver.

[0006] The present invention is further configured as follows: the multi-camera synchronization module first ensures that the data streams of all cameras are aligned at the same timestamp to provide synchronized image input, and then the image denoising module performs denoising on these synchronized images to improve image clarity and quality.

[0007] The present invention is further configured as follows: the dynamic range adjustment module optimizes the quality of the image under different lighting conditions to ensure that the image is always clear regardless of the ambient lighting; the image data after dynamic range adjustment is transmitted to the image compression transmission module, which is compressed to reduce storage requirements and is prepared for real-time transmission to a central processing unit or a cloud; the object detection module first uses the YOLO model to identify various objects (such as vehicles, pedestrians, etc.) in the image in real time to provide basic data for environmental perception; the lane departure warning module uses these data and lane line detection results to evaluate whether the vehicle has deviated from the lane and issues a warning to the driver; the blind spot monitoring module monitors the blind spot of the vehicle in real time to detect whether there are other vehicles or pedestrians; the driving state monitoring module pays attention to the driver's attention and fatigue state to ensure that the driver maintains the best driving state.

[0008] The present invention is further configured as follows: the data compression and encryption module first compresses and encrypts the image data, and then the compressed and encrypted data are transmitted to the distributed storage module; the data label and index module adds labels and indexes to the stored image data, and the historical data analysis module uses these labels and indexes to analyze past data to generate statistical reports and trend forecasts.

[0009] The present invention is further configured as follows: the adaptive cruise module detects the speed and distance of the vehicle in front in real time through a camera and a laser radar, and automatically adjusts the vehicle speed to maintain a safe distance; the automatic parking module combines the camera and ultrasonic sensor data to realize the automatic parking function; the collision warning avoidance module evaluates the front and rear collision risks in real time, and takes risk avoidance measures when necessary, such as automatic braking or steering; the intelligent navigation module provides road recognition and navigation suggestions to help the driver choose a safe and efficient driving route.

[0010] The present invention is further configured to include the following steps: S1, image acquisition and preprocessing; S2, real-time image processing and recognition; S3, data storage and management; S4. Image acquisition and preprocessing.

[0011] The present invention is further configured as follows: in step S1: S1.1, Camera data synchronization: Perform time synchronization on all cameras to ensure that the data streams are aligned at the same timestamp; S1.2, Image denoising: Apply denoising algorithms such as convolutional neural network (CNN) to denoise image data; S1.3, exposure and contrast adjustment: dynamically adjust the exposure and contrast parameters of the image according to the current lighting environment; S1.4. Image compression and transmission preparation: Use image compression algorithms to reduce the amount of data while retaining key image details, encrypt the data and transmit it to a central processing unit or the cloud.

[0012] The present invention is further configured as follows: in step S2: S2.1. Target object recognition: Apply deep learning models such as YOLO to analyze image data in real time, identify and classify different target objects; S2.2, Lane detection and warning: Use image processing technology to identify lane lines, calculate the position of the vehicle relative to the lane lines, determine whether it deviates and issue a warning; S2.3, Blind spot monitoring and warning: Through the side and rear cameras, the system analyzes image data, identifies potential dangers in the blind spots, and promptly alerts the driver; S2.4 Driver status monitoring: Use facial recognition and posture analysis technology to detect the driver's concentration and signs of fatigue, and provide corresponding safety reminders.

[0013] The present invention is further configured as follows: in step S3: S3.1. Data compression and security processing: Use compression algorithms to reduce data volume, and use encryption algorithms to protect data security and prevent unauthorized access; S3.2, Efficient distributed storage: Distribute and store processed data on multiple nodes to ensure high availability and fast access to data; S3.3, Data labeling and indexing: Label the images according to their content and attributes, and establish an efficient indexing structure; S3.4. Historical data analysis and report generation: Based on the stored historical image data, use tags and indexes for analysis to generate useful statistical reports and trend forecasts.

[0014] The present invention is further configured as follows: in step S4: S4.1, Adaptive speed control: Use camera and lidar data to calculate the speed and distance of the vehicle in front and dynamically adjust the vehicle speed; S4.2, Automatic Parking Assist: Combines camera and sensor data to calculate the best parking path and automatically controls the vehicle to perform parking operations; S4.3, Collision risk warning and avoidance: Analyze front and rear camera and radar data, assess collision risk, and automatically perform emergency braking or steering to avoid collision; S4.4, Intelligent Navigation Guidance: The camera recognizes road signs and traffic conditions, combines real-time traffic information, optimizes driving routes and provides navigation guidance.

[0015] The beneficial effects of the present invention are: The present invention significantly improves driving safety, driving stability and data management efficiency through efficient image acquisition, real-time analysis, data management and intelligent assisted driving functions. The system uses multi-camera synchronization technology and convolutional neural network (CNN) denoising algorithm to ensure that high-quality images are captured in various environments, and the dynamic range adjustment module optimizes the image quality under different lighting conditions. The real-time image analysis and recognition module combines deep learning models (such as YOLO) to detect vehicles, pedestrians and traffic signs in real time, and timely detects potential dangers through lane departure warning and blind spot monitoring modules, while the driving status monitoring module ensures that the driver is always in the best state. The data storage and management module improves data security through compression and encryption technology, distributed storage technology ensures high data availability and fast access, data labeling and indexing module makes data retrieval efficient, and historical data analysis module provides statistical reports and trend forecasts. The intelligent assisted driving module combines camera, lidar and sensor data to achieve adaptive cruise, automatic parking, collision warning avoidance and intelligent navigation, improving driving safety and efficiency. Multiple modules work together to achieve full-process management from image acquisition, preprocessing, real-time analysis to data storage management and intelligent assisted driving, providing comprehensive intelligent driving support and data-driven decision support, and further optimizing vehicle maintenance strategies and driving behavior guidance.

[0016] The present invention comprehensively improves driving safety and driving experience through the collaborative work of multiple steps and modules. In the image acquisition and preprocessing stage, the system improves the clarity and quality of the image through camera data synchronization, convolutional neural network (CNN) noise reduction algorithm and dynamic range adjustment module, and can capture high-quality images even under different lighting conditions. It also reduces storage requirements and ensures data security through image compression and encryption processing. In the real-time image processing and recognition stage, the system uses deep learning models (such as YOLO) to identify target objects, help identify and classify target objects such as vehicles, pedestrians and traffic signs, lane departure warning modules and blind spot monitoring modules detect potential dangerous factors in lanes and blind spots in real time, and issue warnings to drivers in time. The driving status monitoring module ensures that the driver concentrates and avoids accidents caused by fatigue. In the data storage and management stage, data compression and encryption technology are used to improve storage efficiency and security. Distributed storage technology ensures high availability and fast access to data. The data tag and index module makes data retrieval more efficient. The historical data analysis module generates statistical reports and trend forecasts to provide decision support for vehicle maintenance and driving behavior. In the intelligent assisted driving stage, the system combines camera and lidar data to achieve adaptive cruise control, the automatic parking module automatically calculates the best parking path, the collision warning and avoidance module evaluates the collision risk in real time and takes risk avoidance measures, and the intelligent navigation module integrates high-definition maps and real-time traffic information to optimize driving routes and provide accurate navigation suggestions. In summary, this on-board camera management method achieves full-process optimization through efficient image acquisition, real-time analysis, data management and intelligent assisted driving functions, significantly improves driving safety, driving stability and data management efficiency, provides comprehensive intelligent driving support and data-driven decision support, and greatly optimizes vehicle maintenance strategies and driving behavior guidance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the system in the present invention.

[0018] Figure 2 Schematic diagram of the method flow in the present invention. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0020] Example 1

[0021] like Figure 1As shown, a vehicle-mounted camera management system includes an intelligent image acquisition and processing module for synchronously acquiring high-quality image data through multiple cameras and preprocessing the image data, a real-time image analysis and recognition module for real-time analysis and recognition of the acquired image data, extracting useful information, and providing real-time feedback based on the analysis results, a data storage and management module for efficiently storing and managing the acquired image data, and an intelligent assisted driving module for providing the driver with an intelligent assisted driving function using the results of image analysis and recognition; The intelligent image acquisition and processing module includes a multi-camera synchronization module for managing and synchronizing data streams of multiple cameras on the vehicle to ensure that all image data are aligned at the same timestamp, an image denoising module for using a convolutional neural network to reduce noise in the image and improve the clarity and quality of the image, a dynamic range adjustment module for automatically adjusting the dynamic range of the camera under different lighting conditions to ensure that clear images can be captured in strong light or low light environments, and an image compression transmission module for effectively compressing high-resolution images while retaining key details and transmitting them in real time to a central processing unit (CPU) or the cloud for further processing and storage; The real-time image analysis and recognition module includes an object detection module for detecting and identifying various objects in an image in real time using a deep learning model YOLO, a lane departure warning module for detecting lane lines through image processing technology, evaluating whether the vehicle has deviated from the lane, and issuing a warning to the driver in a timely manner, a blind spot monitoring module for monitoring the vehicle's blind spots in real time through side and rear cameras, detecting whether there are other vehicles or pedestrians in the blind spots, and issuing a warning when necessary, and a driving status monitoring module for monitoring the driver's attention and fatigue status through facial recognition and posture analysis technology, and promptly reminding the driver to pay attention to safe driving; The intelligent assisted driving module includes an adaptive cruise module for detecting the speed and distance of the vehicle ahead in real time by combining camera and laser radar data to realize the functions of automatically adjusting the vehicle speed and maintaining a safe distance; an automatic parking module for combining camera and ultrasonic sensor data to realize the automatic parking function and help the driver park safely in a small parking space; a collision warning avoidance module for using front and rear camera and radar data to evaluate the collision risk in real time and take risk avoidance measures such as automatic braking or steering when necessary; and an intelligent navigation module for integrating high-definition maps and real-time traffic information, identifying road signs and traffic conditions through cameras, and providing intelligent navigation suggestions to the driver; The multi-camera synchronization module first ensures that the data streams of all cameras are aligned at the same timestamp to provide synchronized image inputs. Next, the image denoising module denoises these synchronized images to improve image clarity and quality. The dynamic range adjustment module optimizes the quality of the image under different lighting conditions to ensure that the image is always clear regardless of the ambient lighting. The image data after dynamic range adjustment is passed to the image compression transmission module, which compresses it to reduce storage requirements and prepares for real-time transmission to the central processing unit or the cloud; the object detection module first uses the YOLO model to identify various objects (such as vehicles, pedestrians, etc.) in the image in real time to provide basic data for environmental perception, and the lane departure warning module uses these data and lane line detection results to evaluate whether the vehicle has deviated from the lane and warns the driver; the blind spot monitoring module monitors the blind spot of the vehicle in real time to detect whether there are other vehicles or pedestrians, and the driving status monitoring module pays attention to the driver's attention and fatigue status to ensure that the driver maintains the best driving state; The data compression and encryption module first compresses and encrypts the image data, and then transmits the compressed and encrypted data to the distributed storage module; the data label and index module adds labels and indexes to the stored image data, and the historical data analysis module uses these labels and indexes to analyze past data and generate statistical reports and trend forecasts; The adaptive cruise control module detects the speed and distance of the vehicle ahead in real time through cameras and lidars, and automatically adjusts the vehicle speed to maintain a safe distance. The automatic parking module combines camera and ultrasonic sensor data to realize automatic parking function. The collision warning and avoidance module evaluates the front and rear collision risks in real time and takes avoidance measures such as automatic braking or steering when necessary. The intelligent navigation module provides road recognition and navigation suggestions to help the driver choose a safe and efficient driving route.

[0022] In the above embodiment, the vehicle-mounted camera management system is an intelligent system integrating multiple advanced technologies, which aims to provide high-quality image acquisition, preprocessing, real-time analysis and recognition, data storage management and intelligent assisted driving functions through the collaborative work of multiple cameras. The system first ensures that the data streams of multiple cameras are aligned at the same timestamp through the multi-camera synchronization module, and uses the image denoising module to improve the clarity and quality of the image. The dynamic range adjustment module optimizes the image quality under different lighting conditions, and the processed image data is compressed by the compression transmission module and transmitted to the central processing unit or the cloud. The real-time image analysis and recognition module uses a deep learning model to detect objects, and evaluates whether the vehicle deviates from the lane through the lane departure warning module. The blind spot monitoring module monitors the vehicle blind spot in real time, and the driving status monitoring module focuses on the driver's attention and fatigue status. The data storage and management module improves the efficiency and security of data storage through data compression and encryption, the distributed storage module ensures data reliability and access speed, the data label and index module adds labels and indexes to the data, and the historical data analysis module generates statistical reports and trend forecasts. The intelligent assisted driving module combines camera and lidar data to achieve adaptive cruise control, the automatic parking module uses camera and ultrasonic sensor data to achieve automatic parking, the collision warning and avoidance module evaluates and avoids collision risks in real time, and the intelligent navigation module provides optimized driving routes and navigation suggestions. The multiple modules work together to achieve full process management from image acquisition, preprocessing, analysis and recognition to data storage management and intelligent assisted driving, improving driving safety and driving experience.

[0023] Example 2

[0024] like Figure 1-2 As shown, a vehicle-mounted camera management method includes the following steps: S1, image acquisition and preprocessing; S2, real-time image processing and recognition; S3, data storage and management; S4, image acquisition and preprocessing; In step S1: S1.1, Camera data synchronization: Perform time synchronization on all cameras to ensure that the data streams are aligned at the same timestamp; S1.2, Image denoising: Apply denoising algorithms such as convolutional neural network (CNN) to denoise image data; S1.3, exposure and contrast adjustment: dynamically adjust the exposure and contrast parameters of the image according to the current lighting environment; S1.4, Image compression and transmission preparation: Use image compression algorithms to reduce the amount of data while retaining key image details, encrypt the data and transmit it to the central processing unit or the cloud; In step S2: S2.1. Target object recognition: Apply deep learning models such as YOLO to analyze image data in real time, identify and classify different target objects; S2.2, Lane detection and warning: Use image processing technology to identify lane lines, calculate the position of the vehicle relative to the lane lines, determine whether it deviates and issue a warning; S2.3, Blind spot monitoring and warning: Through the side and rear cameras, the system analyzes image data, identifies potential dangers in the blind spots, and promptly alerts the driver; S2.4 Driver status monitoring: Use facial recognition and posture analysis technology to detect the driver's concentration and signs of fatigue, and provide corresponding safety reminders; In step S3: S3.1. Data compression and security processing: Use compression algorithms to reduce data volume, and use encryption algorithms to protect data security and prevent unauthorized access; S3.2, Efficient distributed storage: Distribute and store processed data on multiple nodes to ensure high availability and fast access to data; S3.3, Data labeling and indexing: Label the images according to their content and attributes, and establish an efficient indexing structure; S3.4, Historical data analysis and report generation: Based on the stored historical image data, use tags and indexes to analyze and generate useful statistical reports and trend forecasts; In step S4: S4.1, Adaptive speed control: Use camera and lidar data to calculate the speed and distance of the vehicle in front and dynamically adjust the vehicle speed; S4.2, Automatic Parking Assist: Combines camera and sensor data to calculate the best parking path and automatically controls the vehicle to perform parking operations; S4.3, Collision risk warning and avoidance: Analyze front and rear camera and radar data, assess collision risk, and automatically perform emergency braking or steering to avoid collision; S4.4, Intelligent Navigation Guidance: The camera recognizes road signs and traffic conditions, combines real-time traffic information, optimizes driving routes and provides navigation guidance.

[0025] In the above embodiment, the vehicle-mounted camera management method realizes the whole process management from image acquisition, preprocessing, real-time analysis and recognition, data storage management to intelligent assisted driving through a series of steps, thereby improving driving safety and driving experience. First, the system performs image acquisition and preprocessing, ensures that the image data is aligned at the same timestamp through camera data synchronization, applies denoising algorithms such as convolutional neural network (CNN) to improve image clarity, and dynamically adjusts image exposure and contrast according to the lighting environment. Finally, the image is compressed, encrypted and prepared for transmission to the central processing unit or the cloud. In the real-time image processing and recognition stage, the system uses deep learning models (such as YOLO) to identify target objects, analyze image data in real time, identify and classify different target objects, identify lane lines and determine whether the vehicle deviates through image processing technology, use side and rear cameras to monitor potential dangerous factors in blind spots, and monitor the driver's attention and fatigue status through facial recognition and posture analysis technology. In the data storage and management stage, the system uses compression and encryption algorithms to protect data security, distributes and stores processed data on multiple nodes to ensure high availability and fast access, establishes an efficient data retrieval structure through tags and indexes, and performs historical data analysis to generate statistical reports and trend forecasts. Finally, in the intelligent assisted driving stage, the system calculates the speed and distance of the vehicle in front through camera and lidar data, realizes adaptive speed control, combines camera and sensor data for automatic parking assistance, analyzes front and rear camera and radar data to assess collision risk and automatically implements risk avoidance measures, recognizes road signs and traffic conditions through cameras, optimizes driving routes and provides navigation guidance in combination with real-time traffic information. Through these steps, this vehicle-mounted camera management method realizes comprehensive image processing and intelligent driving assistance functions, greatly improving driving safety and efficiency.

[0026] Working principle: When the present invention is in use, the vehicle-mounted camera management system and method realizes full-process management from image acquisition, preprocessing, real-time analysis and recognition, data storage management to intelligent assisted driving through the collaborative work of multiple steps and multiple modules, thereby significantly improving driving safety and driving experience.

[0027] First, during the image acquisition and preprocessing phase, the system uses a multi-camera synchronization module to ensure that the data streams of multiple cameras are aligned at the same timestamp, and then applies denoising algorithms such as convolutional neural networks (CNN) to reduce image noise and improve image clarity and quality. Under different lighting conditions, the dynamic range adjustment module automatically optimizes the exposure and contrast of the image to ensure that the image is always clear. The processed image data is compressed and encrypted by the compression transmission module, ready to be transmitted to the central processing unit or the cloud for further processing and storage.

[0028] In the real-time image processing and recognition stage, the system uses deep learning models (such as YOLO) to identify target objects, analyze image data in real time, and identify and classify different target objects, such as vehicles, pedestrians, and traffic signs. Object information is used in the lane departure warning module, which uses image processing technology to identify lane lines, evaluate whether the vehicle has deviated from the lane, and issue warnings in time. The blind spot monitoring module uses side and rear cameras to monitor the vehicle's blind spots in real time, identify potential dangers, and alert the driver when necessary. The driving status monitoring module uses facial recognition and posture analysis technology to monitor the driver's concentration and fatigue to ensure that the driver remains in the best condition.

[0029] In the data storage and management stage, the system first compresses and encrypts the image data through the data compression and encryption module to protect data security and reduce storage space requirements. The processed data is distributed and stored on multiple nodes to ensure high availability and fast access to the data. The data tag and index module adds tags and indexes to the image data to facilitate subsequent rapid retrieval and management. The historical data analysis module uses these tags and indexes to analyze the stored data, generate statistical reports and trend forecasts, and provide data support for vehicle maintenance and driving behavior analysis.

[0030] In the intelligent assisted driving stage, the system combines camera and lidar data, and the adaptive cruise module detects the speed and distance of the vehicle in front in real time, and automatically adjusts the speed to maintain a safe distance. The automatic parking module combines camera and ultrasonic sensor data to provide automatic parking function to help drivers park safely in small parking spaces. The collision warning and avoidance module uses front and rear camera and radar data to assess the risk of collision in real time, and take risk avoidance measures such as automatic braking or steering when necessary. The intelligent navigation module combines high-definition maps and real-time traffic information, recognizes road signs and traffic conditions through cameras, and provides drivers with optimized driving routes and navigation suggestions.

[0031] In summary, the vehicle-mounted camera management system and method realizes the full process management from image acquisition, preprocessing, real-time analysis and recognition, data storage management to intelligent assisted driving through the collaborative work of multiple modules, which effectively improves driving safety and driving experience.

[0032] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A vehicle-mounted camera management system, characterized in that: It includes an intelligent image acquisition and processing module for synchronously acquiring high-quality image data through multiple cameras and preprocessing the image data, a real-time image analysis and recognition module for real-time analysis and recognition of the acquired image data, extracting useful information, and providing real-time feedback based on the analysis results, a data storage and management module for efficiently storing and managing the acquired image data, and an intelligent assisted driving module for providing the driver with intelligent assisted driving functions using the results of image analysis and recognition; The intelligent image acquisition and processing module includes a multi-camera synchronization module for managing and synchronizing data streams of multiple on-board cameras to ensure that all image data are aligned at the same timestamp, an image denoising module for using a convolutional neural network to reduce noise in an image and improve image clarity and quality, a dynamic range adjustment module for automatically adjusting the dynamic range of the camera under different lighting conditions to ensure that clear images can be captured in strong light or low light environments, and an image compression transmission module for effectively compressing high-resolution images while retaining key details and transmitting them in real time to a central processing unit or the cloud for further processing and storage; The real-time image analysis and recognition module includes an object detection module for detecting and identifying various objects in an image in real time using a deep learning model YOLO, a lane departure warning module for detecting lane lines through image processing technology, evaluating whether the vehicle has deviated from the lane, and issuing a warning to the driver in a timely manner, a blind spot monitoring module for monitoring the vehicle's blind spots in real time through side and rear cameras, detecting whether there are other vehicles or pedestrians in the blind spots, and issuing a warning when necessary, and a driving status monitoring module for monitoring the driver's attention and fatigue status through facial recognition and posture analysis technology, and promptly reminding the driver to pay attention to safe driving; The intelligent assisted driving module includes an adaptive cruise module for combining camera and lidar data to detect the speed and distance of the vehicle in front in real time, and realize the functions of automatically adjusting the vehicle speed and maintaining a safe distance; an automatic parking module for combining camera and ultrasonic sensor data to realize the automatic parking function and help the driver park safely in a small parking space; a collision warning avoidance module for using front and rear camera and radar data to evaluate the collision risk in real time and take risk avoidance measures such as automatic braking or steering when necessary; and an intelligent navigation module for integrating high-definition maps and real-time traffic information, identifying road signs and traffic conditions through cameras, and providing intelligent navigation suggestions to the driver.

2. The vehicle-mounted camera management system according to claim 1, characterized in that: The multi-camera synchronization module first ensures that the data streams of all cameras are aligned at the same timestamp to provide synchronized image inputs. Next, the image denoising module performs denoising on these synchronized images to improve image clarity and quality.

3. The vehicle-mounted camera management system according to claim 1, characterized in that: The dynamic range adjustment module optimizes the quality of the image under different lighting conditions to ensure that the image is always clear regardless of the ambient lighting. The image data after dynamic range adjustment is passed to the image compression transmission module, which is compressed to reduce storage requirements and prepared for real-time transmission to the central processing unit or the cloud; the object detection module first uses the YOLO model to identify various objects in the image in real time to provide basic data for environmental perception, and the lane departure warning module uses these data and lane line detection results to evaluate whether the vehicle has deviated from the lane and warns the driver; the blind spot monitoring module monitors the vehicle's blind spot in real time to detect whether there are other vehicles or pedestrians, and the driving status monitoring module pays attention to the driver's attention and fatigue status to ensure that the driver maintains the best driving state.

4. The vehicle-mounted camera management system according to claim 1, characterized in that: The data compression and encryption module first compresses and encrypts the image data, and then the compressed and encrypted data are transmitted to the distributed storage module; the data label and index module adds labels and indexes to the stored image data, and the historical data analysis module uses these labels and indexes to analyze past data and generate statistical reports and trend forecasts.

5. The vehicle-mounted camera management system according to claim 1, characterized in that: The adaptive cruise control module detects the speed and distance of the vehicle ahead in real time through cameras and lidars, and automatically adjusts the vehicle speed to maintain a safe distance. The automatic parking module combines camera and ultrasonic sensor data to realize automatic parking function. The collision warning and avoidance module evaluates the front and rear collision risks in real time and takes avoidance measures such as automatic braking or steering when necessary. The intelligent navigation module provides road recognition and navigation suggestions to help the driver choose a safe and efficient driving route.

6. A vehicle-mounted camera management system according to any one of claims 1 to 5, and a method of using the same, characterized in that: The following steps are involved: S1, image acquisition and preprocessing; S2, real-time image processing and recognition; S3, data storage and management; S4. Image acquisition and preprocessing.

7. A vehicle-mounted camera management method according to claim 6, characterized in that: In step S1: S1.1, Camera data synchronization: Perform time synchronization on all cameras to ensure that the data streams are aligned at the same timestamp; S1.2, Image denoising: Apply convolutional neural network denoising algorithm to perform denoising on image data; S1.3, exposure and contrast adjustment: dynamically adjust the exposure and contrast parameters of the image according to the current lighting environment; S1.4, Image compression and transmission preparation: Use image compression algorithms to reduce the amount of data while retaining key image details, encrypt the data and transmit it to the central processing unit or cloud.

8. The vehicle-mounted camera management method according to claim 6, characterized in that: In step S2: S2.

1. Target object recognition: Apply deep learning models such as YOLO to analyze image data in real time, identify and classify different target objects; S2.2, Lane detection and warning: Use image processing technology to identify lane lines, calculate the position of the vehicle relative to the lane lines, determine whether it deviates and issue a warning; S2.3, Blind spot monitoring and warning: Through the side and rear cameras, the system analyzes image data, identifies potential dangers in the blind spots, and promptly alerts the driver; S2.4 Driver status monitoring: Use facial recognition and posture analysis technology to detect the driver's concentration and signs of fatigue, and provide corresponding safety reminders.

9. The vehicle-mounted camera management method according to claim 6, characterized in that: In step S3: S3.

1. Data compression and security processing: Use compression algorithms to reduce data volume, and use encryption algorithms to protect data security and prevent unauthorized access; S3.2, Efficient distributed storage: Distribute and store processed data on multiple nodes to ensure high availability and fast access to data; S3.3, Data labeling and indexing: Label the images according to their content and attributes, and establish an efficient indexing structure; S3.

4. Historical data analysis and report generation: Based on the stored historical image data, use tags and indexes for analysis to generate useful statistical reports and trend forecasts.

10. The vehicle-mounted camera management method according to claim 6, characterized in that: In step S4: S4.1, Adaptive speed control: Use camera and lidar data to calculate the speed and distance of the vehicle in front and dynamically adjust the vehicle speed; S4.2, Automatic Parking Assist: Combines camera and sensor data to calculate the best parking path and automatically controls the vehicle to perform parking operations; S4.3, Collision risk warning and avoidance: Analyze front and rear camera and radar data, assess collision risk, and automatically perform emergency braking or steering to avoid collision; S4.4, Intelligent Navigation Guidance: The camera recognizes road signs and traffic conditions, combines real-time traffic information, optimizes driving routes and provides navigation guidance.

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