A multi-class defect detection method for substations based on the improved YOLOv10n

Ensuring the stable operation of power substations is critical for maintaining the reliability of the electrical grid. However, automated inspection of substation equipment remains challenging because multi-class defects are often small, visually blurred, and located in complex backgrounds.

To improve the localization accuracy of small defects with fuzzy features, this paper proposes YOLO-SMALLNET, an improved defect detection algorithm based on YOLOv10n. First, a Detail Information Extraction Convolution module is used to replace the strided convolution modules in the baseline network to preserve fine-grained information during downsampling. Second, a low-level feature fusion detection layer is introduced to reduce small-target feature loss. Third, a Weighted Hybrid Fusion Pyramid Network is adopted to optimize multi-scale feature integration. Finally, a Content-Guided Attention mechanism is integrated to enhance critical defect information while suppressing background noise.

Experimental results show that, compared with the baseline model, YOLO-SMALLNET improves Precision, Recall, mAP@0.5, and mAP@0.5:0.95 by 7.3, 8.2, 3.9, and 3.3%, respectively.

The proposed method effectively reduces false detections and missed detections of small defect regions and is suitable for real-time automated inspection of substation equipment.