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RSP-YOLOv11n multi-module optimized algorithm for insulator defect detection in UAV images.

Bin Zheng1, Niwat Angkawisittpan2, Lu Huang1

  • 1Faculty of Electrical Engineering, Hunan Mechanical & Electrical Polytechnic, Changsha, Hunan, China.

Scientific Reports
|October 10, 2025
PubMed
Summary
This summary is machine-generated.

A new method, RSP-YOLOv11n, improves Unmanned Aerial Vehicle (UAV) inspection by accurately detecting insulator defects. This advanced model enhances detection accuracy and reduces missed faults in power line maintenance.

Keywords:
Insulator defect detectionP2 detection headRSP-YOLOv11nUAV inspection

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Transmission line insulator defects are critical for reliable power grid operation.
  • Unmanned Aerial Vehicle (UAV) inspection is essential for efficient and safe maintenance.
  • Accurate defect identification in UAV imagery is challenging due to small targets and surface variations.

Purpose of the Study:

  • To propose a novel deep learning model, RSP-YOLOv11n, for enhanced insulator defect detection in UAV images.
  • To improve detection accuracy and reduce missed detections compared to existing methods.
  • To validate the model's performance on diverse insulator datasets and real-world inspection scenarios.

Main Methods:

  • Developed RSP-YOLOv11n by modifying the YOLOv11n architecture.
  • Integrated the RCSOSA unit for multi-scale feature extraction.
  • Employed the SEA attention mechanism for improved surface defect detection.
  • Added a P2 detection head to enhance small target detection capabilities.

Main Results:

  • RSP-YOLOv11n demonstrated superior performance over other YOLO models on a custom insulator dataset.
  • Achieved improved precision (92.3%), recall (85.9%), F1-score (89.0%), mAP@0.5 (91.2%), and mAP@0.5:0.95 (61.7%).
  • Outperformed state-of-the-art detectors like DINO and RT-DETR on benchmark datasets (CPLID, IDID, SFID).

Conclusions:

  • RSP-YOLOv11n significantly enhances the accuracy and generalization of insulator defect detection.
  • The model shows strong capabilities in identifying small defects, crucial for UAV inspections.
  • RSP-YOLOv11n is a promising solution for practical, real-world UAV-based power line maintenance.