Intrusion detection method of dynamic hazardous area based on deep learning
In order to solve the problems that the dynamic hazardous area of crane hoisting is difficult to recognize,the loca-tion information is difficult to determine,the workshop production environment is complex,and the personnel target feature in-formation obtained from the special camera perspective is not obvious,which leads to the problems of missed detection and false detection,and the standard target detection model is difficult to deploy and apply,a detection method for the personnel intrusion of dynamic hazardous area based on deep learning was proposed.The binocular vision technology was applied to complete two tasks of heavy lifting judgment and dynamic hazardous area detection of crane hoisting,then the proposed PD-Conv lightweight convolution module and convolutional attention mechanism module were utilized to improve the YOLOv5 base model,and a detection system for the personnel intrusion of dynamic hazardous area in crane hoisting was designed and developed.The results show that the binocular vision technology can accurately obtain the location information of dynamic hazardous area,the improved YOLOv5 basic model can reduce the number of appropriate parameters while ensuring the detec-tion accuracy of the model.The detection system for the personnel intrusion of dynamic hazardous area in crane hoisting can realize the remote control on the intrusion detection task of crane hazardous area,and use the vibration alarm device to con-duct the remind management of intrusion personnel in real time.In the actual test,the missed detection and false detection rate of the system is 2%,which meets the needs of industrial production.The research results can provide reference for the re-search on intrusion detection of dynamic hazardous areas.
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