基于PCSA-YOLOv7 Former的输电线路连接金具及其锈蚀检测方法OA北大核心CSTPCD

The transmission lines are complex in distribution and it is difficult to effectively detect their faults.Among them,the connecting fittings are susceptible to corrosion and other faults due to their long exposure to complex environments.Aiming at the problem that the transmission line connection fitting components are varied in scale and have poor accuracy in detecting their corrosion faults,a detection method is proposed for transmission line connection fittings and their corrosion faults based on dual attention embedding reconstruction and Swin Transformer,i.e.,PCSA-YOLOv7 Former.The experimental results show that the proposed method is superior to 12 existing state-of-the-art object detection algorithms in comprehensive detection performance of the constructed TLCF dataset,with the mAP0.5 of the test set reaching 94.9%.Compared with the baseline model YOLOv7,the proposed method improves the indexes F1 and mAP0.5 by 2.6 percentage points and 2.2 percentage points,respectively,indicating that the proposed method can more comprehensively understand the multi-scale semantic information in the images of transmission line connection fittings and learn their subtle details that are difficult to distinguish.