SACG-YOLO: A Method of Transmission Line Insulator Defect Detection by Fusing Scene-Aware Information and Detailed-Content-Guided Information

To address the challenges in insulator defect detection for transmission lines, including complex background interference, varying defect region scales, and sample imbalance, we propose a detection method that effectively integrates scene perception information and detailed content guidance. First, a scene perception enhancement module is employed to extract global environmental information, improving the baseline model’s adaptability to complex backgrounds. Second, a detailed content attention module is introduced to enable the model to more accurately capture fine-grained features of small defect regions. Furthermore, a normalized Wasserstein distance metric function is adopted to mitigate the sensitivity of the regression branch in the baseline model. Simultaneously, a sample weighting function is utilized to reduce the impact of sample imbalance on the classification branch. Experimental results demonstrate that the proposed method achieves superior detection performance on a real-world transmission line insulator defect dataset.