煤矿安全2024，Vol.55Issue(11)：250-256,7.DOI:10.13347/j.cnki.mkaq.20231919
基于M3CFC-YOLOv7-tiny的矿工乘坐架空乘人装置违章行为识别研究
Research on identifying illegal behavior of miners riding overhead passenger devices based on M3CFC-YOLOv7-tiny
摘要
Abstract
A lightweight miner illegal behavior intelligent detection algorithm based on M3CFC-YOLOv7-tiny is proposed to ad-dress the three challenges of deploying lightweight equipment in coal mines,low recognition precision in complex environment,and imbalanced data samples in the automatic recognition task of miners riding overhead passenger devices.MobileNetV3-Small net-work is introduced into the YOLOv7-tiny model for edge deployment.CBAM attention mechanism is added to enhance the percep-tion and expression ability of miners’behavior characteristics.Improve the loss function to Focal-CIOU to balance the contribution of sample loss.Conduct ablation experiments and comparative experiments on the self-built dataset.The results show that compared with the original model,the parameters of the improved model are reduced by 30.6%,the floating point calculation is reduced by 46.9%,the detection accuracy is increased by 2.3%,and the model lightweight and real-time detection are realized;compared with 9 object detection models,the improved model has the best comprehensive performance in multiple indicators,and there are no missed detections or false detections.关键词
煤矿安全/架空乘人装置/违章行为识别/M3CFC-YOLOv7-tiny/MobileNetV3-Small/CBAM注意力机制/Focal-CIOU损失函数Key words
coal mine safety/overhead passenger device/illegal behavior recognition/M3CFC-YOLOv7-tiny/MobileNetV3-Small/CBAM attention mechanism/Focal-CIOU loss function分类
矿业与冶金引用本文复制引用
卢纪峰,杨超宇..基于M3CFC-YOLOv7-tiny的矿工乘坐架空乘人装置违章行为识别研究[J].煤矿安全,2024,55(11):250-256,7.基金项目
国家自然科学基金资助项目(61873004) （61873004）
