结合TransFormer和复合FPN的YOLOv7tiny绝缘子缺陷检测算法OA

Accurate and rapid inspection and handling of insulator defects contribute to the stable opera-tion of the power system.However,the current defect detection algorithms have problems such as com-plex network structure,long inference time,and poor robustness,which cannot meet the needs of actual inspection.Therefore,a lightweight insulator defect detection algorithm is proposed that combines Trans-Former and composite FPN.This algorithm is based on the YOLOv7 tiny framework,introducing a light-weight EMO with TransFormer architecture as the backbone network.Secondly,a composite FPN struc-ture combining context enhancement and feature refinement is designed for the neck network.Finally,Wise IOU is used as the loss function.The experiment results show that the proposed algorithm Map.5:95 achieves 70.7％,with a detection speed of 69.12frames per second,a model parameter quantity of 4.19M,and an average confidence level of 0.87 for insulator defect detection in the intuitive rendering.This indicates that the proposed algorithm achieves network lightweight,reduces the inference time,and improves detection robustness.