Abstract: At present,there are serious problems when identifying some fault categories in transmission lines,such as insufficient samples and difficulty in locating small targets at long distances captured by drones,resulting in low accuracy in fault identification of transmission lines.To address the above issues,a novel transmission line fault identification method based on transfer learning and improved YOLOv8s is proposed.Firstly,taking YOLOv8s as the baseline,the transfer learning method is used to improve the recognition performance in few-shot,and a bidirectional correlation sample selection module is proposed to obtain sample categories with strong correlation with the target domain,avoiding the problem of negative transfer that may occur when using transfer learning,effectively improving the fault recognition performance of the model.Secondly,aiming at the difficult problem of small target localization,after fusing the 80*80 output feature map and the shallow feature map,EMA is introduced into multi-scale attention to enhance the feature information of the small target for designing small target attention detection layer.To improve the loss function,the CIoU loss is replaced by NWD loss in the prediction frame regression loss,which solves the problem that IoU is sensitive to small target position deviation.Specifically,the Wasserstein distance is used to measure the similarity between prediction box and the truth box.Experimental results show that in the case of few shot and small targets,the proposed method has a mAP of 51.1% in the transmission line fault dataset,which is 8.2% higher than that of the YOLOv8s baseline model,effectively improving the accuracy of fault recognition and providing new solution and method for few-shot and small target transmission line fault identification.

Key words: Fault identification, YOLOv8s, Transfer learning, Sample selection, Attention mechanism, Loss function