兵工自动化2025，Vol.44Issue(4)：37-43,7.DOI:10.7690/bgzdh.2025.04.008
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
张杨 1张雪琴2
作者信息
- 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
- 折叠
摘要
Abstract
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Key words
electric power safety/safety equipment detection/target overlap/feature fusion/Transformer/loss function/lightweighting backbone分类
计算机与自动化引用本文复制引用
张杨,张雪琴..PTB-YOLOv8s:轻量级离散分布安全装备检测的方法[J].兵工自动化,2025,44(4):37-43,7.
