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

作　　者：问永忠 贾澎涛[2] 夏敏高 张龙刚 王伟峰 WEN Yongzhong;JIA Pengtao;XIA Mingao;ZHANG Longgang;WANG Weifeng(Shaanxi Shanmei Pubai Mining Co.,Ltd.,Weinan 715517,China;College of Computer Science and Technology,Xi'an University of Science and Technology,Xi'an 710054,China;College of Safety Science and Engineering,Xi'an University of Science and Technology,Xi'an 710054,China)

机构地区：[1]陕西陕煤蒲白矿业有限公司,陕西渭南715517 [2]西安科技大学计算机科学与技术学院,陕西西安710054 [3]西安科技大学安全科学与工程学院,陕西西安710054

出　　处：《工矿自动化》2025年第1期31-37,77,共8页Journal Of Mine Automation

基　　金：陕西省重点研发计划(2022QCY−LL−70);陕西省秦创原“科学家+工程师”队伍建设项目(2023KXJ−052)。

摘　　要：针对井下危险区域人员监测视频存在光照不均匀、目标尺度不一致、遮挡等复杂情况,基于YOLOv8n网络结构,提出一种改进的井下人员多目标检测算法—YOLOv8n-MSMLAS。该算法对YOLOv8n的Neck层进行改进,添加多尺度空间增强注意力机制(MultiSEAM),以增强对遮挡目标的检测性能;在C2f模块中引入混合局部通道注意力(MLCA)机制,构建C2f-MLCA模块,以融合局部和全局特征信息,提高特征表达能力;在Head层检测头中嵌入自适应空间特征融合(ASFF)模块,以增强对小尺度目标的检测性能。实验结果表明:(1)与Faster R-CNN,SSD,RT-DETR,YOLOv5s,YOLOv7等主流模型相比,YOLOv8n-MSMLAS综合性能表现最佳,mAP@0.5和mAP@0.5:0.95分别达到93.4%和60.1%,FPS为80.0帧/s,参数量为5.80×106个,较好平衡了模型的检测精度和复杂度。(2)YOLOv8n-MSMLAS在光照不均、目标尺度不一致、遮挡等条件下表现出较好的检测性能,适用于现场检测。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,RTDETR,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 

分 类 号：TD323[矿业工程—矿井建设]