IPE-YOLO: A Multi-Scale Defect Detection Method for Power Equipment Inspection

The inspection of power equipment is vital for maintaining the safe and reliable operation of power systems. Among various inspection tasks, the detection of defects in insulators and wind turbine blades holds particular importance. However, existing detection methods often suffer from limited accuracy, largely due to substantial scale variations among defect targets and the loss of features associated with small objects. To address these challenges, this paper proposes Inspection of Power Equipment-YOLO (IPE-YOLO), an enhanced defect detection algorithm based on the YOLOv8n framework. First, a Cross Stage Partial Multi-Scale Edge Information Enhancement (CSP_MSEIE) module is introduced to improve multi-scale feature extraction, enhancing the detection of targets with significant scale diversity while reducing computational complexity. Second, we reconstruct the neck network with a Context-Guided Spatial Feature Reconstruction for Feature Pyramid Networks (CGRFPN), which promotes cross-scale feature fusion and enriches the fine-grained details of small objects, thereby alleviating feature loss in deeper network layers. Finally, a Lightweight Shared Convolutional Detection Head (LSCD) is employed, leveraging shared convolutional layers to decrease model parameters and computational costs without sacrificing detection precision. Experimental results demonstrate that, compared to the baseline YOLOv8n model, IPE-YOLO improves defect detection accuracy for insulators and wind turbine blades by 2.6% and 2.9%, respectively, while reducing the number of parameters by 12.3% and computational costs by 24.7%. These results indicate that IPE-YOLO achieves a superior balance between accuracy and efficiency, making it well-suited for practical engineering deployments in power equipment inspection.