Transmission Line Defect Detection Method Based on Improved YOLOv7

Aiming at the problems of leakage, misdetection and low detection accuracy of transmission line defect detection in UAV power inspection caused by the complex environment of nature, a transmission line defect detection algorithm with improved YOLOv7 is proposed. First, the InceptionNeXt module is introduced to reduce the loss of high-dimensional feature information; second, the CA attention mechanism is integrated with the pooling module to enhance the effective features in the feature extraction process; again, the BiFPN_Add4 module is designed and the feature fusion network is optimized to improve the model's ability of fusing features of different scales; lastly, the F-PIOU loss function is designed to improve the convergence rate of the model. The experimental results show that the improved model improves the mAP value by 3.0%, and the accuracy, recall and detection speed by 3.0%, 3.8% and 8.6%, respectively, compared with the baseline model.