工矿自动化2025，Vol.51Issue(1)：31-37,77,8.DOI:10.13272/j.issn.1671-251x.2024110035
基于改进YOLOv8n的井下人员多目标检测
Multi-target detection of underground personnel based on an improved YOLOv8n model
摘要
Abstract
This study aims to address the complex challenges in monitoring underground personnel in hazardous areas,including uneven lighting,target scale inconsistency,and occlusion.An innovative multi-target detection algorithm,YOLOv8n-MSMLAS,was proposed based on the YOLOv8n network structure.The algorithm modified the Neck layer by incorporating a Multi-Scale Spatially Enhanced Attention Mechanism(MultiSEAM)to enhance the detection of occluded targets.Furthermore,a Hybrid Local Channel Attention(MLCA)mechanism was introduced into the C2f module to create the C2f-MLCA module,which fused local and global feature information,thereby improving feature representation.An Adaptive Spatial Feature Fusion(ASFF)module was embedded in the Head layer to boost detection performance for small-scale targets.Experimental results demonstrated that YOLOv8n-ASAM outperformed mainstream models such as Faster R-CNN,SSD,RT-DETR,YOLOv5s,and YOLOv7 in terms of overall performance,achieving mAP@0.5 and mAP@0.5∶0.95 of 93.4％and 60.1％,respectively,with a speed of 80.0 frames per second,the parameter is 5.80× 106,effectively balancing accuracy and complexity.Moreover,YOLOv8n-ASAM exhibited superior performance under uneven lighting,target scale inconsistency,and occlusion,making it well-suited for real-world applications.关键词
煤矿井下危险区域/井下人员多目标检测/YOLOv8n/多尺度空间增强注意力机制/自适应空间特征融合/轻量化混合局部通道注意力机制Key words
underground hazardous areas in coal mines/multi-target detection of underground personnel/YOLOv8n/multi-scale spatially enhanced attention mechanism/adaptive spatial feature fusion/lightweight hybrid local channel attention mechanism分类
矿业与冶金引用本文复制引用
问永忠,贾澎涛,夏敏高,张龙刚,王伟峰..基于改进YOLOv8n的井下人员多目标检测[J].工矿自动化,2025,51(1):31-37,77,8.基金项目
陕西省重点研发计划(2022QCY-LL-70) （2022QCY-LL-70）
陕西省秦创原"科学家+工程师"队伍建设项目(2023KXJ-052). （2023KXJ-052）
