高电压技术2024，Vol.50Issue(5)：1865-1876,12.DOI:10.13336/j.1003-6520.hve.20231022
基于FasterNet和YOLOv5改进的玻璃绝缘子自爆缺陷快速检测方法
Rapid Detection Method for Self-exploding Defects in Glass Insulators Based on Improved FasterNet and YOLOv5
摘要
Abstract
In order to realize the real-time and fast inspection of insulator defects in power transmission lines,a fast defect detection arithmetic FasterNet-YOLOv5 is proposed by combining FasterNet-tiny and YOLOv5-s-v6.1 network model improvement.Firstly,a FasterNet network with a small number of parameters and faster reasoning speed is introduced to replace the original CSPDarkNet53 backbone network to speed up the detection speed of the network.Then,the DFC-FasterNet module is designed in the backbone feature extraction network by combining the decoupled fully con-nected(DFC)mechanism proposed by the GhostNetv2 network,and the DFC attention mechanism in the module can increase the receptive field during the feature extraction process to improve the detection accuracy of the network.Finally,for the case of glass insulator self-blast defects with smaller targets and more complex background,the Neck module is redesigned,and the BiFPN-F feature fusion module is proposed to enable the network to more accurately localize the in-sulator defect region.The experimental results show that the improved algorithm can locate quickly and accurately,its mean average precision(mAP)reaches 93.3%,which is improved by 5.67%compared with the pre-improvement,and the detection speed reaches 45.7 Hz,which is nearly one times higher than the pre-improvement.Meanwhile,compared with the latest YOLOv8n and YOLOv7-tiny,the improved FasterNet-YOLOv5 has more advantages in detecting the self-destructive defects in terms of accuracy and speed,and the proposed algorithm can locate and identify insulators and their self-destructive defects in real time more quickly.关键词
缺陷检测/BiFPN-F/FasterNet/YOLOv5s/DFC Attention/PConvKey words
defect detection/BiFPN-F/FasterNet/YOLOv5s/DFC Attention/PConv引用本文复制引用
邬开俊,徐泽浩,单宏全..基于FasterNet和YOLOv5改进的玻璃绝缘子自爆缺陷快速检测方法[J].高电压技术,2024,50(5):1865-1876,12.基金项目
甘肃省自然科学基金(23JRRA913) （23JRRA913）
内蒙古自治区重点研发与成果转化计划项目(2023YFSH0043).Project supported by Natural Science Foundation of Gansu Province(23JRRA913),Inner Mongolia Autonomous Region Key R&D and Achievement Trans-formation Program Project(2023YFSH0043). （2023YFSH0043）
