SATF: A Self Attention Transformer Model for Efficient Insulator Defect Detection

Detecting defects in insulators is critical to ensuring transmission reliability and reducing safety risks. Traditional defect detection methods rely on manual feature engineering and classical algorithms, which often struggle to ensure robustness in complex environments. The advent of deep learning has revolutionised insulator defect detection methods by providing superior feature extraction and real-time performance. However, existing deep learning models face challenges in detecting subtle defects. To address these limitations, this paper proposes a Self-Attention Transformer (SATF) detection model tailored for embedded device applications such as drones. The SATF model incorporates a visual transformer in the multilevel CNN architecture to enhance feature representation, a fast spatial pyramid structure for uniform scale information capture, and a deformable self-attention transformer module to improve small object feature understanding of small object features. The experimental results show that the SATF model can detect flashover and damaged insulators with higher accuracy than YOLOv8s by 4.2 % and YOLO11s by 3.8 % while maintaining the computational efficiency, which provides a solution for automatic transmission line detection.