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

Introduction: 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. Methods: 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. Results: 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. Discussion: 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.