SDA-YOLO: Multi-Scale Dynamic Branching and Attention Fusion for Self-Explosion Defect Detection in Insulators

To enhance the performance of UAVs in detecting insulator self-explosion defects during power inspections, this paper proposes an insulator self-explosion defect recognition algorithm, SDA-YOLO, based on an improved YOLOv11s network. First, the SODL is added to YOLOv11 to fuse shallow features with deeper features, thereby improving the model’s focus on small-sized self-explosion defect features. The OBB is also employed to reduce interference from the complex background. Second, the DBB module is incorporated into the C3k2 module in the backbone to extract target features through a multi-branch parallel convolutional structure. Finally, the AIFI module replaces the C2PSA module, effectively directing and aggregating information between channels to improve detection accuracy and inference speed. The experimental results show that the average accuracy of SDA-YOLO reaches 96.0%, which is higher than the YOLOv11s baseline model of 6.6%. While maintaining high accuracy, the inference speed of SDA-YOLO can reach 93.6 frames/s, which achieves the purpose of the real-time detection of insulator faults.