Baode Coal Mine of CHN Energy Shendong Coal Group Co. , Ltd.
School of Electronics and Information Engineering, Tongji University
CCTEG Shanghai Co. , Ltd.

为了能够有效识别煤矿井下工作人员的不安全行为,设计了一种基于轻量级OpenPose算法的井下人员行为智能检测算法。使用轻量级OpenPose网络结构获取红外相机数据中的人体骨骼关键点坐标,然后分别选取不同的骨骼点构建不同的检测算法对摔倒、攀爬以及推搡姿态进行检测。试验结果表明,算法速度达到30f/s,姿态识别整体准确率为86.35%。将行为检测模型部署到工控机并结合报警器,实现了不安全行为的精准实时检测和及时报警提示。

In order to effectively identify the unsafe behavior of underground coal miners, we designed an underground personnel behavior intelligent detection system based on lightweight OpenPose algorithm. The lightweight OpenPose network was used to obtain the coordinates of key points of human skeleton from infrared camera data, and then different recognition algorithms were designed to detect fall, climb and push postures. Experimental results showed that, the algorithm achieved a speed of f/ s and the overall accuracy was 86. 35%. After deploying the detection model to industrial computers and integrating it with alarms, accurate real-time detection and timely alarm notifications for unsafe behaviors was achieved.

阮进林,高鹏,孙源,等.基于轻量级OpenPose的井下人员行为检测算法研究[J].煤炭工程,2024,56(4):150-156.