YOLO11-MG Insulator Fault Detection Based on Multi-Scale Edge Information Selection and Global Information Fusion

Insulator defect detection remains challenging due to low recognition accuracy for aging, breakage, flashover, and similar faults, limited capability in identifying small targets, and inadequate cross-scale feature fusion under complex backgrounds. To address these issues, this study develops an enhanced detection model, YOLO11-MG, which integrates multi-scale edge information selection and enhancement with global information fusion. In the backbone, the original C3K2 module is replaced by C3K2-MSEIS, which mitigates detail loss during downsampling and strengthens feature representation for small objects and blurred boundaries through a multi-scale edge information selection and enhancement strategy. In the neck, a Gather-and-Distribute (GD) mechanism is introduced; by combining Low-GD and High-GD designs, it enables lossless transmission and effective interaction of cross-scale features. Additionally, the C2PSA module is incorporated to realize adaptive feature weight allocation. Experiments demonstrate that YOLO11-MG achieves precision, recall, mAP@0.5, and mAP@50–95 of 82.3%, 96%, 88.1%, and 70.3%, representing improvements of 5.7%, 4%, 3.1%, and 5.6% over the baseline YOLO11s, respectively. Compared with contemporary YOLO variants such as YOLOv8s, YOLOv10m, and YOLOv12m, the proposed model attains a better accuracy–speed trade-off: it improves mAP@0.5 by 6.3% and accelerates inference by 23.4% relative to YOLOv8s, while keeping 27.67 M parameters—on par with lightweight models but with notably stronger global feature interaction and edge preservation. Overall, the method achieves high detection accuracy, computational efficiency, and real-time inference capability, making it well suited for UAV-based inspection and offering reliable technical support for intelligent insulator fault diagnosis in power systems.