摘要
基于深度学习的人员行为识别方法在煤矿井下应用存在对多类别行为识别缺乏系统性分类架构、光线昏暗和低清晰度图像导致细节丢失、矿工姿态和视角差异引发特征形变等问题。提出一种煤矿井下人员行为识别模型CoordEF−YOLOv9t。该模型分别从边缘细节与空间位置特征提取2个方面对YOLOv9t进行改进:YOLOv9t中RepNCSPELAN4模块的卷积操作在捕捉细微或模糊边缘时易导致细节模糊,针对该问题,设计了融合Sobel算子的边缘特征提取模块(EFEM),在RepNCSPELAN4模块中嵌入EFEM,增强主干网络与颈部网络对人体边缘细节的感知能力。传统卷积神经网络难以感知位置信息并充分学习人员位置与动作的空间特征,针对该问题,在颈部网络末端引入坐标卷积,提升模型对人员行为位置信息的感知能力。实验结果表明,CoordEF−YOLOv9t精确率P为73.4%,召回率R为73.7%,mAP@0.5为74.8%,mAP@0.5:0.95为61.1%,相较于YOLOv9t分别提升1.2%,3.2%,1.0%,2.1%;与RT−DETR,YOLOv11,YOLOv12等主流模型相比,CoordEF−YOLOv9t综合性能更优,能更精准地识别煤矿井下人员行为。
Deep learning-based methods for personnel behavior recognition in underground coal mines suffer from several issues,including the lack of a systematic classification framework for multi-class behavior recognition,detail loss caused by dim lighting and low-resolution images,and feature deformation due to differences in miner posture and viewpoint.An underground coal mine personnel behavior recognition model,CoordEF-YOLOv9t,was proposed.The model improved YOLOv9t in two aspects:edge feature extraction and spatial position feature extraction.In YOLOv9t,the convolution operation of the RepNCSPELAN4 module tends to cause detail blurring when capturing subtle or fuzzy edges.To address this problem,an Edge Feature Extraction Module(EFEM)fused with the Sobel operator was designed and embedded into the RepNCSPELAN4 module,enhancing the ability of the backbone and neck networks to perceive the edge details of human bodies.Traditional convolutional neural networks have difficulty perceiving positional information and fully learning the spatial features of personnel location and action.To address this issue,coordinate convolution was introduced at the end of the neck network to improve the model's perception of the positional information of personnel behavior.The experimental results showed that the precision(P)of CoordEF-YOLOv9t was 73.4%,the recall(R)was 73.7%,the mAP@0.5 was 74.8%,and the mAP@0.5:0.95 was 61.1%,which were improvements of 1.2%,3.2%,1.0%,and 2.1%,respectively,compared with YOLOv9t.Compared with mainstream models such as RT-DETR,YOLOv11,and YOLOv12,CoordEF-YOLOv9t demonstrates superior overall performance and can more accurately recognize underground personnel behavior in coal mines.
作者
潘红光
卫泽尘
雷心宇
姚超修
蒋泽
张立斌
PAN Hongguang;WEI Zechen;LEI Xinyu;YAO Chaoxiu;JIANG Ze;ZHANG Libin(.College of Electrical and Control Engineering,Xi'an University of Science and Technology,Xi'an 710054,China;CCTEG Changzhou Research Institute,Changzhou 213015,China;Tiandi(Changzhou)Automation Co.,Ltd.,Changzhou 213015,China)
出处
《工矿自动化》
北大核心
2025年第8期59-66,共8页
Journal of Mine Automation
基金
国家自然科学基金联合基金(区域创新发展联合基金)重点支持项目(U24A2092)
陕西省教育厅科学研究计划服务地方专项——产业化培育项目(23JC049)
天地(常州)自动化股份有限公司科研项目(2024GY0002)。
作者简介
潘红光(1983-),男,山东临沂人,副教授,博士,研究方向为模型预测控制、人工智能及其应用,E-mail:hongguangpan@163.com。
