Chenghe Mining Co. ,Ltd. ,Shaanxi Coal and Chemical Industry Group
CCTEG Changzhou Research Institute
College of Electrical and Control Engineering,Xi’an University of Science and Technology

为防范潜在隐患、保障煤矿安全生产,对矿井作业人员行为进行检测已成为提高矿井安全管理水平的重要方式。鉴于目前常用的智能检测方法精度普遍较低,提出基于S3DD-YOLOv8n的矿工行为检测算法:为提取视频数据的时间信息并保持连续性,在YOLOv8n的骨干网络中引入3D空洞卷积,改进数据增强算法;引入压缩—激励SE(Squeeze&Excitation)注意力机制,提高网络对重点信息的关注程度;引入可变形卷积提高模型对矿工行为的拟合度。经DsLMF+数据集实验验证,该算法的平均精度均值mAP50达到了97.0%,相比YOLOv8n提升了4.0%,同时精确率P和回归率R分别提升了12.9%、7.0%,达到92.5%、90.4%,该算法可高效、精准地检测矿工行为。

In order to prevent potential hazards and ensure coal mine safety,it has become an important way to improve the level of mine safety management to detect the behavior of mine operators. In view of the low accuracy of the commonly used intelligent detection methods,a detection algorithm for miner behavior based on S3DD-YOLOv8n was proposed. Firstly,in order to extract the time information of the video data and maintain the time continuity,3D dilated convolution was introduced into the backbone network of YOLOv8n to improve the data enhancement algorithm. Secondly,the SE (Squeeze & Excitation) attention mechanism was introduced to improve the network’ s attention on key information. Finally, the deformable convolution was introduced to improve the fitting degree of the model to the miner behavior. According to the experimental verification of DsLMF+ data set,the average accuracy mAP50 of the algorithm reaches 97. 0%,which is 4. 0% higher than that of YOLOv8n. At the same time,the accuracy rate P and the regression rate R increased by 12. 9% and 7. 0% respectively,reaching 92. 5% and 90. 4%. The algorithm can detect the miner behavior efficiently and accurately.

陕西省自然科学基础研究计划项目(2024JC-YBQN-0726)

李海川,贺星亮,贾仟国,等.基于S3DD-YOLOv8n的矿工行为检测算法[J].矿业安全与环保,2024,51(5):96-104.

LI Haichuan,HE Xingliang,JIA Qianguo,et al. Detection algorithm for miner behavior based on S3DD-YOLOv8n[ J].Mining Safety & Environmental Protection,2024,51(5):96-104.