Defect Detection of Power Line Insulator Image Based on Self-Supervised Pretraining and YOLOv11

Insulators are crucial components in power transmission lines, serving essential insulation functions. UAV-based intelligent inspection significantly enhances the efficiency of insulator defect detection via image recognition. However, the scarcity of labeled insulator defect data hinders training, the small size of defect targets reduces recognition accuracy, and the computational complexity of recognition models poses challenges for drone deployment. To address these issues, firstly, a self-supervised pretraining method termed Insu-MIM is proposed, which uses a large amount of unlabeled insulator data to pretrain the encoder and decoder, acquiring prior knowledge transferable to downstream detection tasks, thereby improving small-sample generalization. Furthermore, an improved YOLOv11 is presented by adding a small object detection layer and incorporating the NWD loss function, enhancing the model’s ability to detect small insulator defects. Lastly, KD-GCP model compression method is suggested, which employs group channel pruning to remove redundant channels and incorporates knowledge distillation to improve the accuracy of the pruned model. Experimental results demonstrate that our proposed approach increases the insulator defect recognition accuracy by 9.5% compared to the original YOLOv11 method, while reducing computational cost by 43%, significantly enhancing the accuracy and efficiency of insulator inspections in power transmission lines.