Insulators Self-Explosion Detection Based on CA Attention and Adaptive Weighted Feature Fusion

Abstract Insulators are often significantly impacted by target occlusion in complex environments during UAV inspections. To address this issue, an insulator fault detection algorithm based on YOLOv5 is proposed, into which an attention mechanism and cross-scale adaptive weighted feature fusion are incorporated. To suppress interference from complex backgrounds, a Coordinate Attention (CA) module is added to the backbone network. Additionally, the original FPN + PAN structure is replaced with a BiFPN to achieve more effective multi-scale feature fusion. To tackle false detections and missed detections caused by target occlusion, an adaptive weighted feature fusion module is introduced. Moreover, a Power IoU loss with an tuned power factor is used to improve bounding box regression. Furthermore, ghost convolution is integrated into the YOLOv5 backbone to improve detection speed. To verify the algorithm’s effectiveness, training and test samples were prepared using data collected by a power department over three years of UAV inspections. The proposed algorithm was compared with three mainstream algorithms. Results show that our algorithm accurately detects insulator faults under various complex conditions with excellent real-time performance.