电气技术2024，Vol.25Issue(8)：11-17,7.
基于Transformer与信息融合的绝缘子缺陷检测方法
Insulator defect detection method based on Transformer and information fusion
摘要
Abstract
Aiming at the existing insulator aerial images,which have complex backgrounds and are difficult to detect flashover and broken defects,a global and local information fusion(GLIF)-you only look once v8s(YOLOv8s)insulator detection algorithm is proposed.The algorithm uses EfficientFormerV2 as the backbone network to improve the model's ability to extract global information.A feature enhancement module(FEM)is designed based on global and local information to reduce the loss of deep network information through information fusion.Ablation experiments and comparison experiments are carried out on insulators defects dataset,and the experimental results show that the proposed algorithm achieves 77.5%class-wide average accuracy,and its flashover and broken defect detection accuracy reaches 67.7%and 73.5%.Compared with other mainstream algorithms,the detection frame confidence of the proposed algorithm is higher.关键词
绝缘子/缺陷检测/YOLOv8s/TransformerKey words
insulators/defect detection/YOLOv8s/Transformer引用本文复制引用
陈天航,曾业战,邓倩,钟春良..基于Transformer与信息融合的绝缘子缺陷检测方法[J].电气技术,2024,25(8):11-17,7.基金项目
湖南省自然科学基金(2020JJ4276) （2020JJ4276）
