基于Transformer与信息融合的绝缘子缺陷检测方法OA

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.