基于改进YOLOv8n的采掘工作面小目标检测方法

Small object detection method for mining face based on improved YOLOv8n

为有效检测和识别煤矿井下采掘工作面人员是否佩戴安全防护装置,针对井下光照条件差、安全防护装备目标尺寸小且颜色与背景相似等情况,提出了一种基于改进YOLOv8n的采掘工作面小目标检测方法。在YOLOv8n骨干网络C2f模块中融合动态蛇形卷积(DSConv),构建C2f−DSConv模块,以提高模型提取多尺度特征的能力;在Neck层引入极化自注意力(PSA)机制,以减少信息损失,提高特征表达能力;在Head层增设1个专门针对小目标的检测头,形成4检测头结构,以扩大模型检测范围。实验结果表明,改进YOLOv8n模型对井下人员及其所佩戴安全帽、矿灯、口罩、自救器检测的平均精度分别为98.3%,95.8%,89.9%,87.2%,90.8%,平均精度均值为92.4%,优于Faster R−CNN,YOLOv5s,YOLOv7,YOLOv8n模型,且检测速度达208帧/s,满足煤矿井下目标检测精度和实时性要求。

In order to effectively detect and recognize whether the personnel on the mining face in coal mines are wearing safety protection devices,a small object detection method based on improved YOLOv8n is proposed.It is applied in situations such as poor underground lighting conditions,small object sizes of safety protection device,and similar colors to the background.The method integrates Dynamic Snake Convolution(DSConv)into the C2f module of YOLOv8n backbone network to construct a C2f DSConv module,in order to enhance the model's capability to extract multi-scale features.The method introduces polarized self-attention(PSA)mechanism in the Neck layer to reduce information loss and improve feature expression capability.The method adds one detection head specifically designed for small objects at the Head layer,forming a four detection head structure to expand the detection range of the model.The experimental results show that the improved YOLOv8n model has an average precision of 98.3%,95.8%,89.9%,87.2%,and 90.8%for detecting underground personnel and their safety helmets,mining lights,masks,and self rescue devices,respectively.The average precision is 92.4%,which is better than Faster R-CNN,YOLOv5s,YOLOv7,and YOLOv8n models.The detection speed reaches 208 frames per second,meeting the requirements of object detection precision and real-time performance in coal mines.