GSD-YOLO: an improved YOLOv8n for insulator defect detection

In this paper, an improved model GSD-YOLO built upon YOLOv8n is proposed to improve the precision and efficiency in detecting insulator defects. The model introduces three key improvements, namely GSConv lightweight convolution, small target detection Layer, and dynamic detection head (DyHead), which significantly enhance the computational efficiency and detection performance of the model. Experimental results illustrate that the mean average precision (mAP) of the GSD-YOLO model is improved from 87.5% to 91.2% and the recall rate is improved from 81.9% to 84.5%, which significantly outperforms the existing YOLOv8n model while maintaining a low computational complexity. In addition, the model’s ability to detect small-target defects in intricate backgrounds is significantly improved, with a minimum 2% increase in the mean average precision, which effectively adapts to the demands of complex power system environments and better satisfies the practical requirements of power inspections.