High-Performance and Lightweight Detection Network for Substation Equipment Defect Detection

In the intelligent inspection of power systems, the detection of equipment defects is confronted with problems such as low background discrimination, multi-scale morphological differences, and the difficulty in identifying small targets and fine-grained defects, which makes it hard for existing models to balance detection accuracy and computational efficiency. To address this, this study proposes an improved lightweight detection framework, GRID-YOLO. This framework enhances the semantic discrimination ability of the backbone network for complex defects by introducing a cross-stage hierarchical multi-cognitive spatial attention module (C2MSA), designs an enhanced multi-scale bidirectional feature pyramid network (EMFPN) to achieve efficient alignment and preservation of cross-level features, and builds a hybrid attention reparameterization fusion module (HAFusion) to dynamically filter and enhance heterogeneous features. Additionally, a grouped distribution focal loss head (GDFLHead) is adopted to strengthen the collaborative optimization between classification and regression tasks. In this study, a dedicated dataset for substation equipment defect detection was constructed, and extensive comparative experiments were conducted. The results show that GRID-YOLO achieves an mAP50 of 78.3%, which is 6.1 percentage points higher than the baseline model. The mAP50–95, Recall, and F1-score reach 52.3%, 73.6%, and 77.6%, respectively, and the inference speed reaches 506 FPS. Compared with various advanced detection models, GRID-YOLO demonstrates strong overall performance and can better meet the actual needs of substation equipment defect detection in the intelligent inspection scenarios of power systems.