Corrosion Detection Method of Transmission Line Components in Mining Area Based on Multiscale Enhanced Fusion

Abstract

Component corrosion is one of the potential safety hazards in transmission lines in mining areas. In order to solve the problem of poor detection accuracy caused by the large proportion of small targets and complex background in the current distant view corrosion inspection task by UAV, we propose a PWR-YOLOV5 detection method for corrosion components based on the YOLOV5 algorithm. Firstly, a new feature fusion network, WA-PANet, is reconstructed on the basis of the path aggregation network (PANet) to make full use of the features at different stages and advance the detection accuracy of small targets in distant view by deepening the process of feature fusion and introducing the skip layer connections and adaptive feature fusion factors. Secondly, the pyramid split attention (PSA) module is introduced into the deep layers of the network to highlight the feature expression of corrosion targets and enhance the ability to detect pixel-level objects. Then, we construct a receptive feature enhancement network (RFENet), which can heighten the feature fusion effect of the WA-PANet and alleviate the problem of the feature expression ability weakening due to the fusion of different receptive field features. Finally, the EIoU Loss is adopted to optimize the loss function and improve the positioning accuracy of the bounding box. The experimental results show that the mAP of the PWR-YOLOV5 algorithm can reach up to 95.37%, which is 5.22% higher than YOLOV5, and the detection speed is 64.9FPS. Compared with the algorithms such as YOLOV4, Faster R-CNN, and YOLOX, the improved algorithm has better overall detection performance for the corrosion components of transmission lines in the mining area.