CN114137634A - Platform end intrusion detection method and system based on radar video fusion - Google Patents
Platform end intrusion detection method and system based on radar video fusion Download PDFInfo
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Abstract
本发明提供一种基于雷达视频融合的站台端头入侵检测方法及系统，通过在前端将视频数据采集和雷达数据采集融合，规避单个传感器工作的弊端和不足，能够不受任何天气、光线环境的影响，即使在浓烟或浓雾的情况下，依然可以可靠的进行人体的检测与跟踪；实现快速发现，快速定位，监测功能不受任何影响，可全天候全方位站台端头入侵，识别效率和准确度均大大超过人工能力；通过对采集到的雷达数据、视频图像序列进行自动分析计算，通过识别各类光照阴影、雨雪天气、飞鸟来识别排除误报，能够准确实时识别出站台端头入侵的异常事件，并发出智能声光报警，帮助车站提升站台旅客安全管理的工作效率和用户服务体验。
The present invention provides a method and system for intrusion detection at a station head based on radar video fusion. By integrating video data collection and radar data collection at the front end, the disadvantages and deficiencies of single sensor operation can be avoided, and the system can be protected from any weather and light environment. Even in the case of thick smoke or fog, the human body can still be detected and tracked reliably; it can realize rapid discovery, rapid positioning, and the monitoring function is not affected in any way. The accuracy greatly exceeds the human ability; through the automatic analysis and calculation of the collected radar data and video image sequences, and by identifying various types of light and shadows, rain and snow weather, and flying birds to identify and eliminate false alarms, the platform terminal can be accurately identified in real time. Abnormal events of intrusion, and intelligent sound and light alarms are issued to help stations improve the work efficiency and user service experience of platform passenger safety management.
Description
Technical Field
The invention relates to the technical field of radar, in particular to a platform end intrusion detection method and system based on radar video fusion.
Background
At present, although a video camera of an existing railway platform end intrusion alarm system adopting video detection can accurately sense personnel intrusion, due to the optical characteristics of the video camera, the video camera is very easily interfered by the surrounding environment, for example, severe weather such as dense fog, smoke, storm, rain, snow, glare, total darkness and the like or illumination weather conditions can obviously influence the normal work of the video camera, so that the acquired image information result is not accurate enough, the detection accuracy of intelligent events is low, and all-weather detection cannot be supported.
Although the existing railway platform end intrusion alarm system adopting infrared temperature sensing equipment can overcome the problem of weak light at night, the detection distance is generally less than 10 meters, and meanwhile, the system is particularly easy to be interfered by the environment, so that missing reports can be generated when the temperature of a human body is low in winter, and false reports can be easily generated when small animals pass by.
The existing platform end detection system is generally a data isolated island, although each system has more data collection amount, the data application rate is low or even no application exists, the data lacks systematic analysis, and the system can not perform version iteration upgrade optimization according to the used condition.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a method for detecting station end intrusion based on radar video fusion, which is used to solve the problems existing in the conventional system for detecting station end intrusion.
To achieve the above and other related objects, the present invention provides a system for detecting intrusion at a station end based on radar video fusion, comprising: the system comprises a millimeter wave radar, a camera, a network switch, an RFID reader-writer and a solar power supply system; the millimeter wave radar and the camera are both connected with the network switch, and the network switch and the RFID reader-writer are both connected with the solar power supply system;
the millimeter wave radar is used for acquiring radar data of the railway platform and judging whether intruders exist according to the radar data;
the video camera is used for acquiring video data of the railway platform and judging whether intruders exist according to the video data;
the RFID reader-writer is used for verifying whether the intruder is a worker when the millimeter wave radar and the camera output the intruder.
Optionally, the system further comprises an LED display screen, wherein the LED display screen is connected to the solar power supply system and used for flashing an LED when the result output by the RFID reader is a non-worker.
Optionally, the system further comprises a network sound, wherein the network sound is connected with the solar power supply system and used for outputting alarm voice when the result output by the RFID reader-writer is a non-working person.
Optionally, the system further comprises an edge computing intelligent terminal, and the edge computing intelligent terminal is connected with the solar power supply system and used for carrying out data transmission with the cloud platform.
The invention also provides a platform end intrusion detection method based on radar video fusion, which comprises the following steps:
step 1: judging whether the self-checking of the detection system passes or not; if the self-checking is passed, entering the step 2; if the self-test is not passed, repeating the step 1;
step 2: judging whether the millimeter wave radar detects radar data of the intrusion signal; if the radar data of the intrusion signal is detected, entering step 3; if the radar data of the intrusion signal is not detected, repeating the step 2;
and step 3: judging whether the classification attribute of the intrusion signal is human or not by using the millimeter wave radar; if the classification attribute of the intrusion signal is judged to be personnel intrusion, entering step 4; if the classification attribute of the intrusion signal is judged to be a non-personnel condition, entering step 5;
and 4, step 4: collecting and analyzing the collected video data by using a camera, and carrying out intelligent detection on the invasion of the personnel for the second time; if the analysis result output by the camera is that personnel intrusion exists, confirming that the personnel intrusion exists for the second time, and entering the step 6; if the analysis result output by the camera is that no personnel invade exists, the second confirmation is false alarm, and the step 5 is carried out;
and 5: judging whether the false alarm is discarded or not, and re-entering a detection state;
step 6: the RFID reader-writer detects whether the intruder works or not; if the result of the determination is that the staff is available, entering a step 7; if the determination result is not that the staff is available, entering step 8;
and 7: automatically alarming, recording the detection result, and reentering the detection state;
and 8: carrying out intrusion alarm of an end area, carrying out LED flicker by using an LED display screen and outputting prompt alarm voice by using a network sound;
and step 9: judging whether the person to be detected leaves the detection area or not; if the person to be detected leaves the detection area, the initial detection state of the detection system is recovered; and if the person to be detected is still in the detection area, repeating the step 8.
Optionally, the method further comprises:
step 11: continuously monitoring whether the detection system is in a data return window or not in the normal operation process of the detection system; if not, continuously circulating the step 11;
step 12: establishing a data uploading and downloading session link by using the edge computing intelligent terminal and the cloud platform;
step 13: judging whether the test session link of the edge computing intelligent terminal is established successfully or not, and if so, entering step 14; if the establishment fails, judging whether the failure times exceed 3 times, and if the failure times exceed 3 times, giving up the session and entering the step 11; if the failure times are not more than 3, entering step 12, and restarting the uploading and downloading session link;
step 14: and uploading the data to a cloud platform by using the edge computing intelligent terminal to perform model labeling training processing.
Optionally, the method further comprises:
step 21: continuously monitoring whether the detection system is in a data return window or not in the normal operation process of the detection system; if not, continuously looping step 21;
step 22: establishing a data uploading and downloading session link by using the edge computing intelligent terminal and the cloud platform;
step 23: judging whether the test session link of the edge computing intelligent terminal is established successfully or not, and if so, entering step 24; if the establishment fails, judging whether the failure times exceed 3 times, and if the failure times exceed 3 times, giving up the session and entering step 21; if the failure times are not more than 3, entering step 22 and re-initiating the upload and download session link;
step 24: downloading the current version from the cloud platform to the location of the edge computing intelligent terminal;
step 25: verifying and confirming the downloaded version packet by using the edge computing intelligent terminal, and if the verification is successful, entering the step 26; if the verification fails, the upgrade is abandoned;
step 26: carrying out version updating operation by utilizing the edge computing intelligent terminal;
step 27: confirming whether the monitoring system is updated or not by using the edge computing intelligent terminal, and entering step 28 if the monitoring system is updated; if the update fails, go to step 29;
step 28: after the current version is upgraded, the edge computing intelligent terminal is used for operating the current version;
step 29: and returning the version to the previous version, and operating the previous version by using the edge computing intelligent terminal.
As described above, the present invention provides a method and a system for detecting platform end intrusion based on radar video fusion, which have the following advantages: the invention integrates the video data acquisition and the radar data acquisition at the front end, avoids the defects and shortcomings of the work of a single sensor, is not influenced by any weather and light environment, and can still reliably detect and track the human body even under the condition of dense smoke or dense fog; the system realizes quick discovery, quick positioning, the monitoring function is not influenced, all-weather all-round platform end invasion can be realized, the recognition efficiency and the accuracy greatly exceed the manual capability, the artificial intelligence algorithm in the field of computer vision and radar is utilized, under the condition of almost no need of manual intervention, radar data acquired by the system and a video image sequence are automatically analyzed and calculated, false alarms are recognized and eliminated by recognizing various illumination shadows, rain and snow weather and flying birds, abnormal events of platform end invasion can be accurately recognized in real time, an intelligent sound-light alarm is given, meanwhile, on-site early warning prompts and event records are given out in the Web and the page of a comprehensive management and control platform, and the work efficiency and the user service experience of platform passenger safety management are improved. Meanwhile, the invention realizes the periodic acquisition and uploading of data to a training optimization model and the periodic version iterative updating through a side cloud cooperative system architecture, and improves the processing real-time performance and accuracy of the railway platform end intrusion detection system; the problems of bandwidth pressure, event time delay and the like of the high-definition camera on a video private network are solved through a side cloud cooperative framework, so that the operation management efficiency of a station is improved, the service level is improved, and the emergency handling capacity of station emergencies is enhanced. In addition, the invention can detect, track and identify and detect suspicious objects aiming at moving objects in radar data and video images, and carry out intelligent analysis and comparison on the objects on the basis, can identify whether personnel enter a platform end detection area, distinguish working personnel and non-working personnel, can shield some false alarms caused by illumination shadows, rain and snow weather, birds and the like, can accurately identify abnormal events invading the platform end in real time, can generate a customized voice alarm system according to the characteristic content detected by the current event, can push the system to a field area for playing in real time, carries out real-time voice alarm prompt, and pushes control information to flash and alarm of an LED screen of a corresponding area in real time. The invention forms all-weather signal acquisition function complementation with high-definition video detection by adding millimeter wave radar detection on the front-end sensing equipment layer, and can normally work under the conditions of dense fog, smoke dust, storm, rain and snow, glare, complete blackness and the like; in a network layer, a side cloud cooperative system architecture is formed by adding an edge computing intelligent event analysis server and a cloud intelligent event analysis server, occupation of a core network and a backbone transmission network is reduced by local distribution of high-bandwidth services, the utilization rate of a communication network is improved, front-end bandwidth pressure is relieved, processing real-time performance of an intrusion detection system at the end of a railway platform is improved by sinking content and computing capacity, and meanwhile, the detection accuracy of the existing system can be timely improved by training and updating a large number of samples and regular version iteration updating based on an AI technology.
Drawings
Fig. 1 is a schematic hardware configuration diagram of a platform end intrusion detection system based on radar video fusion according to an embodiment;
fig. 2 is a schematic flowchart illustrating a method for detecting station end intrusion based on radar video fusion according to an embodiment of the present invention;
fig. 3 is a schematic flow chart illustrating data uploading performed by the platform end intrusion detection system and the cloud platform according to an embodiment of the present invention;
fig. 4 is a flowchart illustrating a version download performed by the platform end intrusion detection system and the cloud platform according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Referring to fig. 1, the present invention provides a platform end intrusion detection system based on radar video fusion, including: the system comprises a millimeter wave radar, a camera, a network switch, an RFID reader-writer and a solar power supply system; the millimeter wave radar and the camera are both connected with the network switch, and the network switch and the RFID reader-writer are both connected with the solar power supply system;
the millimeter wave radar is used for acquiring radar data of the railway platform and judging whether intruders exist according to the radar data;
the video camera is used for acquiring video data of the railway platform and judging whether intruders exist according to the video data;
the RFID reader-writer is used for verifying whether the intruder is a worker when the millimeter wave radar and the camera output the intruder.
According to the record, the platform end intrusion detection system further comprises an LED display screen, wherein the LED display screen is connected with the solar power supply system and used for carrying out LED flashing when the result output by the RFID reader-writer is a non-working person.
According to the record, the platform end intrusion detection system further comprises a network sound which is connected with the solar power supply system and used for outputting alarm voice when the result output by the RFID reader-writer is a non-working person.
According to the record, the platform end intrusion detection system further comprises an edge computing intelligent terminal, and the edge computing intelligent terminal is connected with the solar power supply system and used for carrying out data transmission with the cloud platform.
Therefore, the platform end intrusion detection edge computing system based on radar video fusion provided by the invention has the advantages that the video data acquisition and the radar data acquisition are fused at the front end, the defects and the defects of the work of a single sensor are avoided, the influence of any weather and light environment can be avoided, and the detection and the tracking of a human body can be reliably carried out even under the condition of dense smoke or dense fog; the system realizes quick discovery, quick positioning, the monitoring function is not influenced, all-weather all-round platform end invasion can be realized, the recognition efficiency and the accuracy greatly exceed the manual capability, the artificial intelligence algorithm in the field of computer vision and radar is utilized, under the condition of almost no need of manual intervention, radar data acquired by the system and a video image sequence are automatically analyzed and calculated, false alarms are recognized and eliminated by recognizing various illumination shadows, rain and snow weather and flying birds, abnormal events of platform end invasion can be accurately recognized in real time, an intelligent sound-light alarm is given, meanwhile, on-site early warning prompts and event records are given out in the Web and the page of a comprehensive management and control platform, and the work efficiency and the user service experience of platform passenger safety management are improved.
Meanwhile, the system realizes the periodic acquisition and uploading of data to a training optimization model and the periodic version iterative updating through a side cloud cooperative system architecture, and improves the processing real-time performance and accuracy of the railway platform end intrusion detection system; the problems of bandwidth pressure, event time delay and the like of the high-definition camera on a video private network are solved through a side cloud cooperative framework, so that the operation management efficiency of a station is improved, the service level is improved, and the emergency handling capacity of station emergencies is enhanced.
As shown in fig. 2, the present invention further provides a method for detecting station end intrusion based on radar video fusion by using any of the above detection systems, the method comprising the following steps:
step 1: judging whether the self-checking of the detection system passes or not; if the self-checking is passed, entering the step 2; if the self-test is not passed, repeating the step 1;
step 2: judging whether the millimeter wave radar detects radar data of the intrusion signal; if the radar data of the intrusion signal is detected, entering step 3; if the radar data of the intrusion signal is not detected, repeating the step 2;
and step 3: judging whether the classification attribute of the intrusion signal is human or not by using the millimeter wave radar; if the classification attribute of the intrusion signal is judged to be personnel intrusion, entering step 4; if the classification attribute of the intrusion signal is judged to be a non-personnel condition, entering step 5;
and 4, step 4: collecting and analyzing the collected video data by using a camera, and carrying out intelligent detection on the invasion of the personnel for the second time; if the analysis result output by the camera is that personnel intrusion exists, confirming that the personnel intrusion exists for the second time, and entering the step 6; if the analysis result output by the camera is that no personnel invade exists, the second confirmation is false alarm, and the step 5 is carried out;
and 5: judging whether the false alarm is discarded or not, and re-entering a detection state;
step 6: the RFID reader-writer detects whether the intruder works or not; if the result of the determination is that the staff is available, entering a step 7; if the determination result is not that the staff is available, entering step 8;
and 7: automatically alarming, recording the detection result, and reentering the detection state;
and 8: carrying out intrusion alarm of an end area, carrying out LED flicker by using an LED display screen and outputting prompt alarm voice by using a network sound;
and step 9: judging whether the person to be detected leaves the detection area or not; if the person to be detected leaves the detection area, the initial detection state of the detection system is recovered; and if the person to be detected is still in the detection area, repeating the step 8.
According to the record, the invention provides a platform end intrusion detection method based on radar video fusion, which is used for detecting, tracking and identifying moving objects in radar data and video images and detecting suspicious objects, intelligently analyzing and comparing the moving objects on the basis, identifying whether personnel enter a platform end detection area or not, distinguishing working personnel and non-working personnel, shielding false alarms caused by illumination shadows, rain and snow weather, flying birds and the like, accurately identifying abnormal events of platform end intrusion in real time, generating a customized voice alarm system according to the characteristic content of current event detection, pushing the system to a field area for playing in real time, carrying out real-time voice alarm prompt, and pushing control information to LED screen flashing alarms of corresponding areas in real time.
In an exemplary embodiment, as shown in fig. 3, the method for detecting station head intrusion further includes:
step 11: continuously monitoring whether the detection system is in a data return window or not in the normal operation process of the detection system; if not, continuously circulating the step 11;
step 12: establishing a data uploading and downloading session link by using the edge computing intelligent terminal and the cloud platform;
step 13: judging whether the test session link of the edge computing intelligent terminal is established successfully or not, and if so, entering step 14; if the establishment fails, judging whether the failure times exceed 3 times, and if the failure times exceed 3 times, giving up the session and entering the step 11; if the failure times are not more than 3, entering step 12, and restarting the uploading and downloading session link;
step 14: and uploading the data to a cloud platform by using the edge computing intelligent terminal to perform model labeling training processing.
In an exemplary embodiment, as shown in fig. 3, the method for detecting station head intrusion further includes:
step 21: continuously monitoring whether the detection system is in a data return window or not in the normal operation process of the detection system; if not, continuously looping step 21;
step 22: establishing a data uploading and downloading session link by using the edge computing intelligent terminal and the cloud platform;
step 23: judging whether the test session link of the edge computing intelligent terminal is established successfully or not, and if so, entering step 24; if the establishment fails, judging whether the failure times exceed 3 times, and if the failure times exceed 3 times, giving up the session and entering step 21; if the failure times are not more than 3, entering step 22 and re-initiating the upload and download session link;
step 24: downloading the current version from the cloud platform to the location of the edge computing intelligent terminal;
step 25: verifying and confirming the downloaded version packet by using the edge computing intelligent terminal, and if the verification is successful, entering the step 26; if the verification fails, the upgrade is abandoned;
step 26: carrying out version updating operation by utilizing the edge computing intelligent terminal;
step 27: confirming whether the monitoring system is updated or not by using the edge computing intelligent terminal, and entering step 28 if the monitoring system is updated; if the update fails, go to step 29;
step 28: after the current version is upgraded, the edge computing intelligent terminal is used for operating the current version;
step 29: and returning the version to the previous version, and operating the previous version by using the edge computing intelligent terminal.
In summary, the invention provides a platform end intrusion detection method and system based on radar video fusion, which avoids the defects and shortcomings of single sensor operation by fusing video data acquisition and radar data acquisition at the front end, can not be affected by any weather and light environment, and can still reliably detect and track human body even under the condition of dense smoke or dense fog; the system realizes quick discovery, quick positioning, the monitoring function is not influenced, all-weather all-round platform end invasion can be realized, the recognition efficiency and the accuracy greatly exceed the manual capability, the artificial intelligence algorithm in the field of computer vision and radar is utilized, under the condition of almost no need of manual intervention, radar data acquired by the system and a video image sequence are automatically analyzed and calculated, false alarms are recognized and eliminated by recognizing various illumination shadows, rain and snow weather and flying birds, abnormal events of platform end invasion can be accurately recognized in real time, an intelligent sound-light alarm is given, meanwhile, on-site early warning prompts and event records are given out in the Web and the page of a comprehensive management and control platform, and the work efficiency and the user service experience of platform passenger safety management are improved. Meanwhile, the invention realizes the periodic acquisition and uploading of data to a training optimization model and the periodic version iterative updating through a side cloud cooperative system architecture, and improves the processing real-time performance and accuracy of the railway platform end intrusion detection system; the problems of bandwidth pressure, event time delay and the like of the high-definition camera on a video private network are solved through a side cloud cooperative framework, so that the operation management efficiency of a station is improved, the service level is improved, and the emergency handling capacity of station emergencies is enhanced. In addition, the invention can detect, track and identify and detect suspicious objects aiming at moving objects in radar data and video images, and carry out intelligent analysis and comparison on the objects on the basis, can identify whether personnel enter a platform end detection area, distinguish working personnel and non-working personnel, can shield some false alarms caused by illumination shadows, rain and snow weather, birds and the like, can accurately identify abnormal events invading the platform end in real time, can generate a customized voice alarm system according to the characteristic content detected by the current event, can push the system to a field area for playing in real time, carries out real-time voice alarm prompt, and pushes control information to flash and alarm of an LED screen of a corresponding area in real time. The invention forms all-weather signal acquisition function complementation with high-definition video detection by adding millimeter wave radar detection on the front-end sensing equipment layer, and can normally work under the conditions of dense fog, smoke dust, storm, rain and snow, glare, complete blackness and the like; in a network layer, a side cloud cooperative system architecture is formed by adding an edge computing intelligent event analysis server and a cloud intelligent event analysis server, occupation of a core network and a backbone transmission network is reduced by local distribution of high-bandwidth services, the utilization rate of a communication network is improved, front-end bandwidth pressure is relieved, processing real-time performance of an intrusion detection system at the end of a railway platform is improved by sinking content and computing capacity, and meanwhile, the detection accuracy of the existing system can be timely improved by training and updating a large number of samples and regular version iteration updating based on an AI technology.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
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