Intelligent frontier defense monitoring system and method based on image recognition

Abstract

The invention discloses an intelligent frontier defense monitoring system and method based on image recognition. The intelligent frontier defense monitoring system comprises a monitoring equipment unit, a data transmission unit, a data processing and analysis unit, a threat detection and behavior analysis unit, an alarm and response unit and a data storage unit. According to the invention, the image data of the monitoring area is analyzed and processed in real time by using the artificial intelligence model, abnormal targets, behaviors or events such as illegal invasion and weapon carrying can be automatically identified, an alarm is given in time, and event information is recorded. Through various monitoring devices and an intelligent image recognition technology, efficient monitoring and accurate early warning of a large-range boundary area are achieved, and the automation and intelligent level of frontier defense safety management is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of border security technology, and in particular to a border monitoring system based on image recognition technology. Background Art

[0002] With the frequent cross-border activities around the world, the demand for border security monitoring is increasing. Traditional border monitoring systems mainly rely on manual monitoring and traditional video monitoring equipment, which cannot cope with border threats in large-scale and complex environments. Existing technologies face the following problems: limited monitoring resources, heavy manual patrol tasks, limited coverage, especially in desolate areas, it is difficult to effectively monitor in real time. Information lags. Video monitoring data needs to be manually reviewed, resulting in information lags and failure to timely detect potential cross-border threats or abnormal behaviors. Harsh environmental challenges. At night and in harsh weather conditions, the effectiveness of traditional camera monitoring is greatly reduced, and potential threats cannot be accurately identified. The monitoring system is not intelligent enough. Most current border monitoring systems lack intelligent analysis methods and cannot achieve automatic threat identification and real-time warning. Therefore, border monitoring technology urgently needs to be upgraded. By introducing artificial intelligence and multi-monitoring equipment fusion technology, a smart border monitoring system can be built to improve monitoring coverage, accuracy and warning capabilities. Summary of the invention

[0003] The present invention proposes an intelligent border monitoring system and method based on image recognition, aiming to solve the problems of limited coverage, slow response speed, and insufficient intelligence level in existing border monitoring methods. The present invention uses AI technology, image processing, behavior analysis, and fusion of multiple monitoring devices to achieve real-time monitoring of border areas, target recognition, and abnormal behavior detection, effectively improving the automation and intelligence level of border security.

[0004] The technical solution of the present invention is a smart border monitoring system based on image recognition, comprising the following parts: Monitoring equipment unit: Use a variety of monitoring equipment to collect real-time images or video data of the border area, and input them into the border command center for processing through the data transmission unit; the data fusion of multiple monitoring equipment further improves the target detection capability in complex environments; Data transmission unit: transmits the image or video data collected by the monitoring equipment unit to the border defense command center through the data transmission network; Data processing and analysis unit: pre-processes the image or video data transmitted by the data transmission unit, and then uses an artificial intelligence model to automatically detect and identify the target in the image; Threat detection and behavior analysis unit: Based on target detection, it further analyzes the detected target behavior to determine whether there is abnormal or suspicious behavior. Through behavior analysis and detection, it identifies high-risk behaviors such as illegal border crossing, weapon carrying, and smuggling activities in the border defense area, and marks abnormal behaviors in real time; Alarm and response unit: When a threat is detected, the alarm mechanism is triggered, and real-time threat notification is sent to the border command center through the alarm module; different levels of alarms and emergency response measures are triggered according to the threat level and urgency of the target; according to the threat level, the border command center dispatches drones or patrol personnel to respond on the spot; Data storage unit: All monitoring data and alarm information will be stored in a local or cloud database. The stored data can be used for post-analysis, event backtracking and system optimization.

[0005] Furthermore, the artificial intelligence model includes but is not limited to a target detection algorithm based on a convolutional neural network (CNN), which is used to improve the accuracy of target recognition and anomaly detection.

[0006] Furthermore, system optimization refers to the ability to self-learn through continuously updated monitoring data, iteratively update the artificial intelligence model through accumulated historical data, automatically analyze historical monitoring data according to dynamic changes in border areas or new threats, optimize the recognition ability of artificial intelligence models, adjust model parameters through self-learning, and enhance the response speed and accuracy to similar events.

[0007] Furthermore, the system will regularly update the target library and behavioral analysis capabilities to adapt to the ever-changing threat situation within the border area.

[0008] The intelligent border monitoring method based on image recognition includes the following steps Step 1: Data collection and preprocessing Collect real-time images or video data of the border monitoring area through various monitoring devices installed in the border area, use image preprocessing algorithms to remove noise, enhance contrast and correct distortion to improve image quality and clarity, and improve the accuracy and robustness of monitoring through multi-source information fusion; Step 2: Data Transfer The pre-processed real-time image or video data of the border monitoring area is transmitted to the border command center through the data transmission network; Step 3: AI object detection and recognition The improved YOLO target detection algorithm is combined with the improved Transformer to analyze the pre-processed image data in real time, automatically detect and identify people, vehicles, ships or other suspicious targets in the image, especially for illegal border crossers, suspicious vehicles or other dangerous targets. The system has high-precision recognition capabilities; at the same time, through the artificial intelligence model, the system can effectively distinguish normal and abnormal behaviors, and make intelligent judgments on potential threats for subsequent behavior analysis and judgment; The specific improvement is to introduce a cross-scale attention mechanism (CSAM) to the feature pyramid network (FPN) in YOLO to improve the correlation between feature maps. The improved feature pyramid FPN can more accurately detect large and small targets in the same area at the same time, enhancing the comprehensiveness of the system in complex scenarios.

[0009] The Hybrid Context Attention Module (HCAM) is introduced in Transformer. This module combines the local attention mechanism and the global attention mechanism to ensure that the model can extract clear target features when dealing with complex backgrounds and blurred boundaries. The local attention mechanism allows the model to focus on details and detect small targets; the global attention mechanism is used to maintain the understanding of the overall scene, prevent missed detections and false detections, and enable the artificial intelligence model to better detect distant and blurred targets in complex border environments.

[0010] By introducing the cross-scale attention module and the mixed context attention module, the multi-scale detection and small target detection performance of the artificial intelligence model are enhanced.

[0011] Step 4: Behavioral analysis and anomaly detection After identifying the target, further analyze the target's behavior to determine whether the target has abnormal behavior, further analyze potential threats, and conduct a comprehensive assessment based on historical data to reduce the false alarm rate and automatically determine whether the alarm mechanism needs to be triggered; Step 5: Real-time alarm and remote response When an abnormal target or behavior is identified, the system automatically triggers an alarm. The alarm module automatically sends alarm information to the command center in real time through wireless communication or satellite network, including the specific location, image, video and detailed data of the target's behavior and its behavior analysis results of the suspicious target. The management personnel of the command center make quick decisions based on the alarm information, save the specific location, image, video and detailed data of the target's behavior of the suspicious target, and automatically mobilize nearby patrol personnel or unmanned equipment to arrive at the scene for processing according to the threat level; Step 6: Data storage analysis After an incident occurs, the system stores all relevant images, videos, sensor data, alarm records and other monitoring data in the cloud or local database for future review and analysis to find potential threat sources or improve security strategies; or for use as legal evidence.

[0012] The present invention has the following beneficial effects: 1. The present invention improves the target detection algorithm based on convolutional neural network, enhances the multi-scale detection and small target detection performance of the artificial intelligence model, can more accurately detect the existence of suspicious targets and abnormal behaviors in the border area, reduces the dependence on manual monitoring, and greatly improves the accuracy and timeliness of monitoring.

[0013] 2. The present invention has the ability to integrate multiple monitoring devices and can integrate multi-source data such as cameras, radars, infrared thermal imaging, and drones to further enhance the monitoring effect. The multi-monitoring device fusion technology enables the system to provide reliable monitoring around the clock under complex terrain and severe weather conditions.

[0014] 3. The real-time alarm and remote control functions of the present invention ensure that border management personnel can respond quickly to emergencies, effectively reducing security risks.

[0015] 4. The automatic learning and model optimization functions of the present invention enable the system to continuously adapt to new security threats and maintain continuous and efficient monitoring capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is the overall structural diagram of the intelligent monitoring system of the present invention. DETAILED DESCRIPTION

[0017] The specific implementation method of the present invention provides a detailed description of the overall design of the border monitoring system, equipment deployment, application of artificial intelligence models, and integration of multiple monitoring devices, to ensure large-scale, high-precision border monitoring and rapid response to emergencies.

[0018] The intelligent border monitoring system based on image recognition of the present invention is mainly composed of the following parts: Monitoring equipment unit: includes a variety of border monitoring equipment, such as monitoring cameras, drone shooting devices, infrared thermal imaging, radar, etc. These devices are responsible for collecting real-time image or video data, which are input into the border command center for processing through the data transmission unit. The monitoring camera can work around the clock, has night vision function, and can provide clear image data in low-light or no-light conditions. With the fusion of data from multiple monitoring devices, the system can obtain more comprehensive monitoring data through a variety of monitoring equipment such as radar and infrared thermal imaging. Radar and infrared thermal imaging data will be fused with the image information captured by the camera to further enhance the target detection capability in complex environments, such as effective monitoring in severe weather or night conditions.

[0019] Data transmission unit: transmits the image or video data collected by the monitoring equipment unit to the border control center through the data transmission network. The data transmission network can include wired communication, wireless communication or satellite network to ensure smooth data transmission in complex terrain or remote border areas.

[0020] Data processing and analysis unit: pre-processes the image or video data transmitted by the data transmission unit, including noise elimination, contrast enhancement, etc. Then, an artificial intelligence model is used to automatically detect and identify the target in the image.

[0021] The artificial intelligence model includes but is not limited to a target detection algorithm based on a convolutional neural network (CNN), which is used to improve the accuracy of target recognition and anomaly detection.

[0022] Threat detection and behavior analysis unit: Based on target detection, the system will further analyze the detected target behavior to determine whether there is abnormal or suspicious behavior. Through behavioral analysis detection, the system can identify high-risk behaviors such as illegal border crossing, weapon carrying, smuggling activities in the border area, and mark abnormal behaviors in real time.

[0023] Alarm and response unit: When the system detects a threat, the alarm mechanism is triggered and real-time threat notifications are sent to the border command center through the alarm module. The threat notification information includes images, video clips, and behavioral analysis results of suspicious targets. The alarm mechanism includes a multi-level alarm mechanism, which triggers different levels of alarms and emergency response measures according to the threat level and urgency of the target. According to the threat level, the border command center can dispatch drones or patrol personnel to respond on site. The system also supports remote control of unmanned equipment, such as drones and ground patrol vehicles, to further confirm threats or take actions.

[0024] Data storage unit: All monitoring data and alarm information will be stored in a local or cloud database. The stored data can be used for post-analysis, event backtracking, and system model optimization.

[0025] The following is the specific implementation process of the present invention: (1) Data collection and preprocessing Multiple surveillance cameras, drones and other equipment are deployed in border monitoring areas to collect real-time images or video data. The collected images may be affected by factors such as weather, light, and terrain. Therefore, the original images are first denoised, contrast enhanced, and distortion corrected through image preprocessing algorithms to improve the quality and clarity of the images, providing more accurate input data for the subsequent recognition of artificial intelligence models. In addition to image data, the system can also integrate other monitoring equipment such as radar and infrared thermal imaging. For example, radar can supplement the shortcomings of visual monitoring in bad weather, and infrared thermal imaging can detect thermal radiation signals from human bodies or vehicles at night or in low light environments. The fusion of this information can improve the system's full-time, all-weather monitoring capabilities of border areas, and improve the accuracy and robustness of monitoring through multi-source information fusion.

[0026] Real-time images or video data are collected by surveillance cameras, drones and other equipment installed in border areas, and image preprocessing algorithms are used to remove noise, enhance contrast and correct distortion to improve image quality and subsequent artificial intelligence model recognition accuracy.

[0027] (2) Data transmission The monitoring data from the border area is transmitted to the border command center through the data transmission network. The data transmission network can include wired communication, wireless communication or satellite network to ensure smooth data transmission in complex terrain or remote border areas.

[0028] (3) Artificial Intelligence Target Detection and Recognition It adopts a deep learning target detection algorithm, such as the combination of the improved YOLO target detection algorithm and the improved Transformer. The pre-processed image data is analyzed in real time through the artificial intelligence model, and people, vehicles, ships or other suspicious targets in the image are automatically detected and identified. In particular, for illegal border crossers, suspicious vehicles or other dangerous targets, the system has high-precision recognition capabilities; at the same time, through the artificial intelligence model, the system can effectively distinguish normal behavior from abnormal behavior (classification), and make intelligent judgments on potential threats for subsequent behavior analysis and judgment.

[0029] The specific improvement is that in YOLO, a cross-scale attention module (CSAM) is introduced to the feature pyramid network (FPN) to improve the correlation between feature maps. In traditional FPN, feature maps of different scales are simply weighted layer by layer when fused. CSAM introduces cross-scale interactions in feature maps of different scales, allowing low-resolution feature maps to exchange information with high-resolution feature maps. In this way, low-level feature maps can obtain more contextual information, while high-level feature maps can enhance their detailed expression. Through this multi-level extraction mechanism, the model can capture low-level features (such as edges and contours) at the shallow level and high-level features (such as shape and motion state) at the deep level. The improved feature pyramid FPN can more accurately detect large and small targets in the same area at the same time, enhancing the comprehensiveness of the system in complex scenes. The hybrid context attention module (HCAM) is introduced in the Transformer, which combines local and global attention to ensure that the model can extract clear target features when dealing with complex backgrounds and blurred boundaries. The local attention mechanism allows the model to focus on details and detect small targets; the global attention is used to maintain the understanding of the overall scene, prevent missed detection and false detection, and enable the artificial intelligence model to better detect distant and blurred targets in complex border environments. The present invention enhances the multi-scale detection and small target detection performance of the artificial intelligence model by introducing a cross-scale attention module and a hybrid context attention module.

[0030] (4) Behavior analysis and anomaly detection After identifying the target, the system will further analyze the target's behavior to determine whether the target has abnormal behavior, such as illegally crossing the border, wandering or staying in sensitive areas for a long time, or carrying, and further analyze potential threats. Combined with historical data for comprehensive evaluation to reduce false alarms; the system can automatically determine whether the alarm mechanism needs to be triggered. In addition, the system can also detect weapon carrying behavior and automatically identify the outline or characteristics of the weapon in the image through the artificial intelligence model.

[0031] (5) Real-time alarm and remote response: According to the threat level, the system's alarm mechanism is divided into multiple levels. For example, for low-risk behaviors, the system can only record data and issue warnings; for high-risk behaviors (such as illegal border crossing or carrying weapons), the system will automatically issue a high-priority alarm and recommend immediate action.

[0032] When an abnormal target or behavior is identified, the system automatically triggers an alarm. The alarm module automatically sends alarm information to the command center in real time through wireless communication or satellite network, including the specific location, image, video and detailed data of the target's behavior and its behavior analysis results of the suspicious target. The managers of the command center can make quick decisions based on the alarm information, save the specific location, image, video and detailed data of the target's behavior of the suspicious target, and automatically mobilize nearby patrol personnel or unmanned equipment for on-site processing according to the threat level; (6) Data storage and analysis After an incident occurs, the system stores all relevant images, videos, sensor data, alarm records, and other monitoring data in the cloud or local database for future review, analysis to find potential threat sources or improve security strategies; or as legal evidence; in addition, the system also supports long-term data storage and fast retrieval to ensure that users can quickly find relevant historical data. For example, after a major incident occurs in a border area, managers can use the system to trace back relevant monitoring data, analyze the incident in detail, and submit this data as legal evidence.

[0033] In addition, the system also has automatic learning and optimization functions. It can self-learn through continuously updated monitoring data, iteratively update the artificial intelligence model through accumulated historical data, and automatically analyze historical monitoring data according to dynamic changes in border defense areas or new threats, and optimize the recognition ability of artificial intelligence models. It can adjust model parameters through self-learning to enhance the response speed and accuracy to similar events.

[0034] The system will regularly update the target library and behavior analysis capabilities to adapt to the changing threat situation in the border area. For example, if the system detects new illegal intrusion methods within a certain period of time, the model will automatically adjust and optimize the identification strategy to improve the system's response capabilities and monitoring effects to new threats.

[0035] The system also supports long-term data storage and fast retrieval, ensuring that users can quickly find relevant historical data.

[0036] The system can transmit monitoring data and alarm information to the border control center in real time via wireless network or satellite communication to ensure immediate response.

[0037] The system also has autonomous patrol and intelligent scheduling functions. For example, drones in the monitoring area can automatically patrol according to the set patrol routes and provide real-time feedback on patrol results.

[0038] The system has remote control and intelligent dispatch capabilities. The command center can dispatch drones and patrol cars in real time to achieve real-time monitoring and emergency response in hard-to-reach areas.

[0039] The system can transmit monitoring data and alarm information to the border command center in real time via wireless network or satellite communication to ensure immediate response.

[0040] The system uses encryption technology to protect the transmission and storage security of monitoring data and prevent data leakage or tampering.

[0041] The system can adjust parameters through the human-computer interaction interface, allowing users to customize monitoring strategies and alarm thresholds according to different scenario requirements.

[0042] The system is suitable for military border defense, national customs, border checkpoints and other border security monitoring scenarios, and can be flexibly deployed according to scenario requirements.

[0043] To sum up, the present invention utilizes the integration of artificial intelligence technology and multiple monitoring devices to enhance the detection capabilities of artificial intelligence models, especially the capabilities of multi-scale detection and small target detection, and can realize large-scale, all-weather, and fully automatic border monitoring, effectively improving the intelligence and automation level of border security management.

Claims

1. An intelligent border monitoring system based on image recognition, characterized in that: Includes the following parts, Monitoring equipment unit: uses a variety of monitoring equipment to collect real-time images or video data of the border defense area, and then inputs them into the border defense command center for processing through the data transmission unit; The data fusion of multiple monitoring devices further improves the target detection capability in complex environments; Data transmission unit: transmits the image or video data collected by the monitoring equipment unit to the border defense command center through the data transmission network; Data processing and analysis unit: pre-processes the image or video data transmitted by the data transmission unit, and then uses an artificial intelligence model to automatically detect and identify the target in the image; Threat detection and behavior analysis unit: Based on target detection, the artificial intelligence model further analyzes the detected target behavior to determine whether there is abnormal or suspicious behavior. Through behavioral analysis and detection, it identifies high-risk behaviors such as illegal border crossing, weapon carrying, and smuggling activities in the border defense area, and marks abnormal behaviors in real time; Alarm and response unit: When a threat is detected, the alarm mechanism is triggered, and real-time threat notification is sent to the border command center through the alarm module; different levels of alarms and emergency response measures are triggered according to the threat level and urgency of the target; according to the threat level, the border command center dispatches drones or patrol personnel to respond on the spot; Data storage unit: All monitoring data and alarm information will be stored in a local or cloud database. The stored data can be used for post-analysis, event backtracking and system optimization.

2. The intelligent border monitoring system based on image recognition according to claim 1 is characterized in that: The artificial intelligence model includes but is not limited to a target detection algorithm based on a convolutional neural network (CNN), which is used to improve the accuracy of target recognition and anomaly detection.

3. The intelligent border monitoring system based on image recognition according to claim 2 is characterized in that: System optimization means the ability to self-learn through continuously updated monitoring data, iteratively update the artificial intelligence model through accumulated historical data, automatically analyze historical monitoring data according to dynamic changes in border areas or new threats, optimize the recognition ability of the artificial intelligence model, adjust model parameters through self-learning, and enhance the response speed and accuracy to similar events.

4. The intelligent border monitoring system based on image recognition according to claim 3 is characterized in that: The system will regularly update the target library and behavioral analysis capabilities to adapt to the ever-changing threat situation in the border area.

5. The monitoring method of the monitoring system according to any one of claims 1 to 4, characterized in that: The following steps are included Step 1: Data collection and preprocessing Collect real-time images or video data of the border monitoring area through various monitoring devices installed in the border area, use image preprocessing algorithms to remove noise, enhance contrast and correct distortion to improve image quality and clarity, and improve the accuracy and robustness of monitoring through multi-source information fusion; Step 2: Data Transfer The pre-processed real-time image or video data of the border monitoring area is transmitted to the border command center through the data transmission network; Step 3: AI object detection and recognition By combining the improved YOLO target detection algorithm with the improved Transformer, the pre-processed image data is analyzed in real time through the artificial intelligence model, and people, vehicles, ships or other suspicious targets in the image are automatically detected and identified. In particular, the system has high-precision recognition capabilities for illegal border crossers, suspicious vehicles or other dangerous targets. At the same time, through the artificial intelligence model, the system effectively distinguishes normal and abnormal behaviors, and makes intelligent judgments on potential threats for subsequent behavior analysis and judgment; Step 4: Behavioral Analysis and Anomaly Detection After identifying the target, further analyze the target's behavior to determine whether the target has abnormal behavior, further analyze potential threats, and conduct a comprehensive assessment based on historical data to reduce the false alarm rate and automatically determine whether the alarm mechanism needs to be triggered; Step 5: Real-time alarm and remote response When an abnormal target or behavior is identified, the system automatically triggers an alarm. The alarm module automatically sends alarm information to the command center in real time through wireless communication or satellite network, including the specific location, image, video and detailed data of the target's behavior and its behavior analysis results of the suspicious target. The management personnel of the command center make quick decisions based on the alarm information, save the specific location, image, video and detailed data of the target's behavior of the suspicious target, and automatically mobilize nearby patrol personnel or unmanned equipment to arrive at the scene for processing according to the threat level; Step 6: Data storage analysis After an incident occurs, the system stores all relevant images, videos, sensor data, alarm records and other monitoring data in the cloud or local database for future review and analysis to find potential threat sources or improve security strategies; or use as legal evidence.

6. The monitoring method according to claim 5, characterized in that: The specific improvements to the combination of the YOLO target detection algorithm and the improved Transformer include the introduction of a cross-scale attention module (CSAM) to the feature pyramid FPN in YOLO to improve the correlation between feature maps. The improved feature pyramid FPN can more accurately detect large and small targets in the same area at the same time, enhancing the comprehensiveness of the system in complex scenarios. The hybrid contextual attention module HCAM is introduced in Transformer. This module combines the local attention mechanism and the global attention mechanism to ensure that the model can extract clear target features when dealing with complex backgrounds and blurred boundaries. The local attention mechanism allows the model to focus on details and detect small targets; the global attention mechanism is used to maintain the understanding of the overall scene, prevent missed detections and false detections, and enable the artificial intelligence model to better detect distant and blurred targets in complex border environments. By introducing the cross-scale attention module and the mixed context attention module, the multi-scale detection and small target detection performance of the artificial intelligence model are enhanced.

7. The monitoring method according to claim 5, characterized in that: The system supports long-term data storage and fast retrieval, ensuring that users can quickly find relevant historical data.

8. The monitoring method according to claim 5, characterized in that: The system also has automatic learning and optimization functions. It can self-learn through continuously updated monitoring data and iteratively update the artificial intelligence model through accumulated historical data. It can automatically analyze historical monitoring data according to dynamic changes in border areas or new threats, optimize the recognition ability of artificial intelligence models, adjust model parameters through self-learning, and enhance the response speed and accuracy to similar incidents.

9. The monitoring method according to claim 5, characterized in that: The system has remote control and intelligent dispatch capabilities. The command center can dispatch drones and patrol cars in real time to achieve real-time monitoring and emergency response in hard-to-reach areas.

10. The monitoring method according to claim 5, characterized in that: The system adjusts parameters through a human-computer interaction interface, allowing users to customize monitoring strategies and alarm thresholds according to different scenario requirements.

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