PTB-YOLOv8s:轻量级离散分布安全装备检测的方法  

PTB-YOLOv8s:A Lightweight Method for Safety Equipment Detection Based on Discrete Distribution

作　　者：张杨 张雪琴[2] Zhang Yang;Zhang Xueqin(He’nan Vocational College of Applied Technology,Zhengzhou 450042,China;Xi’an Jiaotong University City College,Xi’an 710018,China)

机构地区：[1]河南应用技术职业学院,郑州450042 [2]西安交通大学城市学院,西安710018

出　　处：《兵工自动化》2025年第4期37-43,共7页Ordnance Industry Automation

摘　　要：针对电力人工巡检维修工作中,安全帽和高空作业安全带等关键安全装备合规使用的监测效率低等问题,提出一种基于YOLOv8s的检测模型,旨在降低人工成本,提升检测效率。通过设计C2f_PTB特征提取模块,结合Transformer的全局信息捕获和卷积神经网络的局部特征提取能力,提升模型对小尺寸和分散目标的检测效率;引入标准化高斯瓦瑟斯坦距离(normalized Gaussian Wasserstein distance,NGWD)损失函数,增强模型对微小安全装备检测的稳定性与准确性;设计基于StarNet的轻量化主干网络C2f_star模块,降低网络参数。实验结果表明,改进后的模型在电力安全装备数据集上的mAP达到93.7%,相比基准模型检测精度提升5.6%,检测速度提升10帧/秒,证明所提出的方法能够有效提升检测效果。In view of the low monitoring efficiency of compliance use of key safety equipment such as safety helmet and safety belt for working at heights in manual inspection and maintenance of electric power,a detection model based on YOLOv8s is proposed to reduce labor costs and improve detection efficiency.By designing the C2f_PTB feature extraction module,combining the global information capture of Transformer and the local feature extraction ability of convolutional neural network,the detection efficiency of the model for small size and scattered targets is improved;The normalized Gaussian Wasserstein distance(NGWD)loss function is introduced to enhance the stability and accuracy of the model for the detection of small safety equipment;Lightweight backbone network C2f_star module based on StarNet is designed to reduce network parameters.Experimental results show that the mAP of the improved model reaches 93.7%on the power safety equipment data set,the detection accuracy is improved by 5.6%and the detection speed is improved by 10 frames per second compared with the benchmark model,which proves that the proposed method can effectively improve the detection effect.

关 键 词：电力安全 安全装备检测 目标重叠 特征融合 TRANSFORMER 损失函数 主干轻量化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]