What question did this study set out to answer?

The aim is to enhance insulator defect detection in UAV inspections by integrating multimodal data into an improved YOLOv11 model.

April 19, 2026Open Access

Aerial insulator defect detection method based on CWSP-YOLO

Key Points

The aim is to enhance insulator defect detection in UAV inspections by integrating multimodal data into an improved YOLOv11 model.
Developed an improved YOLOv11 model with cross-modal collaboration and wavelet optimization.
Incorporated channel attention and PIoU v2 for dynamic gradient optimization.
Conducted experiments using a self-built dataset of aerial images.
Achieved a mean average precision of 84.77%.
Obtained an accuracy of 94.53%.
Recorded a recall rate of 82.38%.
Functioned at 24 frames per second, meeting real-time inspection requirements.

Abstract

Abstract With the rapid evolution of UAV technology, intelligent power equipment inspection via aerial imagery is critical. To address traditional insulator defect detection bottlenecks (high false/missed detection, insufficient multimodal fusion), this paper proposes an improved YOLOv11-based model integrated with multimodal data, featuring cross-modal collaboration, wavelet-optimized C3k2, channel attention, and PIoU v2-based dynamic gradient optimization. Experiments on a self-built dataset show it achieves 84.77% mean average precision, 94.53% accuracy, 82.38% recall, with 24 FPS meeting real-time requirements, offering reliable support for UAV power inspection.

Aerial insulator defect detection method based on CWSP-YOLO

Key Points

Abstract

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