Research on a Multi-Object Detection Method for Substation Equipment Defects Based on YOLOv8 and Attention Mechanisms

To address challenges in substation equipment defect detection—such as severe background interference, large variations in defect scales, and imbalanced sample distribution—this study focuses on a 500 kV substation and proposes an improved YOLOv8 model by integrating coordinate attention (CA) and an adaptive spatial attention gating unit. This enhancement optimizes feature extraction and multi-scale fusion strategies. Experimental results demonstrate that the proposed method significantly improves the simultaneous detection capability of both small-scale and large-scale defects under complex environmental conditions, providing effective technical support for intelligent operation and maintenance of power equipment.