Design and Implementation of Deep Learning-Based Abnormal Behavior Detection System

The mining environment of molybdenum is highly complex and dynamic, with a large number of safety risks related to personnel operation and equipment operation. Traditional safety monitoring methods mainly rely on manual inspections, which are inefficient and unable to achieve real-time monitoring and precise early warning. To address the above challenges and enhance the safety management level of Chifeng Molybdenum Mine, this study designed and implemented an abnormal behavior recognition system based on deep learning. This system adopts an edge-cloud collaborative architecture, organically integrating the front end and the back end. The back end utilizes Node-RED for low-code process orchestration, integrates EdgeX to achieve unified access of heterogeneous devices, adopts EMQX to manage large-scale device communication, realizes real-time video stream distribution through MediaMTX, and introduces the optimized YOLOv8 model for AI reasoning and analysis. The system, based on computer vision technology, has achieved real-time monitoring of key safety elements such as face recognition, safety helmet detection, and distance monitoring for hot work operations. The experimental results show that the accuracy rate of abnormal behavior detection by the system in complex mining environments is significantly better than that of traditional monitoring methods.