Abstract: Insulator faults in transmission lines directly threaten power grid stability. To accurately identify insulator faults, an insulator fault diagnosis method based on an improved YOLOv3(You Only Look Once v3)- Tiny framework is proposed. Firstly, a channel-spatial attention mechanism is integrated into the feature extraction network to enhance critical feature capture capabilities. Subsequently, a CSP-RFB ( Cross Stage Partial-Receptive Field Block) module is designed to improve small-target detection performance while reducing computational complexity. Finally, a novel loss function is adopted to optimize localization accuracy. Experimental results demonstrate that the enhanced YOLOv3-Tiny algorithm achieves up to 97. 4% MAP(Mean Average Precision) in insulator fault detection, significantly outperforming the original YOLOv3-Tiny model.

Key words: deep learning, defect detection, insulator fault diagnosis