工矿自动化2025，Vol.51Issue(1)：11-20,103,11.DOI:10.13272/j.issn.1671-251x.2024110002
基于改进YOLOv8模型的井下人员入侵带式输送机危险区域智能识别
Intelligent recognition of personnel intrusion into belt conveyor hazardous areas based on an improved YOLOv8 model
摘要
Abstract
To address challenges such as severe dust and fog interference,complex background environments,and variable personnel scales with frequent occlusions in coal mine belt conveyor scenarios,which resulted in low accuracy in recognizing personnel intrusions into hazardous areas,an intelligent recognition system based on an improved YOLOv8 model was proposed.The improved YOLOv8 model enhanced detailed feature extraction by replacing the C2f module in the backbone network with the C2fER module,which improved recognition performance for small targets.The Feature Enhancement Weighted Bi-Directional Feature Pyramid Network(FE-BiFPN)structure was introduced into the neck network to strengthen feature fusion capabilities,thereby enhancing recognition of multi-scale personnel targets.The Separated and Enhancement Attention Module(SEAM)was incorporated to improve the model's attention to local features in complex backgrounds,which boosted its ability to recognize occluded personnel targets.Furthermore,the WIoU loss function was applied to enhance training outcomes,improving recognition accuracy.Ablation experiment results showed that the improved YOLOv8 model achieved a 2.3％increase in accuracy and a 3.4％improvement in mAP@0.5 compared to the baseline YOLOv8s model,with a recognition speed of 104 frames per second.Personnel recognition experiments demonstrated that,compared to YOLOv10m,YOLOv8s-CA,YOLOv8s-SPDConv,and YOLOv8n models,the improved YOLOv8 model delivered superior recognition performance for small,multi-scale,and occluded targets,achieving a recognition accuracy of 90.2％and an mAP@0.5 of 87.2％.Personnel intrusion experiments revealed that the intelligent recognition system achieved an average accuracy of 93.25％in identifying personnel intrusions into belt conveyor hazardous areas,satisfying recognition requirements.关键词
煤矿带式输送机/人员入侵危险区域/YOLOv8模型/遮挡目标检测/小目标检测/多尺度融合/C2fER模块/特征强化加权双向特征金字塔网络结构Key words
coal mine belt conveyor/personnel intrusion into hazardous areas/YOLOv8 model/occluded target detection/small target detection/multi-scale fusion/C2fER module/Feature Enhancement Weighted Bi-Directional Feature Pyramid Network(FE-BiFPN)分类
矿山工程引用本文复制引用
毛清华,苏毅楠,贺高峰,翟姣,王荣泉,尚新芒..基于改进YOLOv8模型的井下人员入侵带式输送机危险区域智能识别[J].工矿自动化,2025,51(1):11-20,103,11.基金项目
陕西省教育厅青年创新团队科研计划项目(23JP094) （23JP094）
陕西省秦创原"科学家+工程师"队伍建设项目(2023KXJ-238) （2023KXJ-238）
陕西省科技厅重点研发计划项目(2024CY2-GJHX-25). （2024CY2-GJHX-25）
