Lightweight Defect Detection in Substations with Multi-Scale Features and Network Pruning

With the increasing adoption of intelligent inspection systems for substation equipment, massive amounts of data are being generated. To address the challenge of balancing detection accuracy and lightweight deployment in current object detection models, this paper proposes YOLOv10-SPD (Substation Power Defect), a high-precision yet lightweight improved model tailored for substation defect detection. Compared to existing methods, the proposed model introduces multiple innovations in structural design and module fusion. (1) A Feature Modulation Module is proposed to significantly enhance the model’s ability to perceive and model defect details. (2) A hybrid module integrating structural information and channel attention is designed to efficiently reconstruct and represent feature maps. (3) A Multi-Scale Context Modeling Module is developed, leveraging shared convolutional kernels to achieve compact expression of multi-scale semantic information. (4) An Efficient Detection Head adopts a hierarchical semantic fusion strategy, further improving recognition accuracy for small and multi-scale targets. (5) A Weight-Magnitude-Based Hierarchical Pruning Strategy is introduced to compress model size and boost inference efficiency while maintaining accuracy. Experiments on a public substation defect dataset demonstrate that the proposed method achieves 94.11% mAP@0.5, outperforming the baseline YOLOv10n by 5.14%, while reducing model parameters by 76.09% and computational costs by 38.82%. The model achieves higher detection accuracy with lower computational overhead, effectively meeting the requirements for efficient and accurate substation defect detection, demonstrating strong practical applicability.