Fully Autonomous Real-Time Defect Detection for Power Distribution Towers: A Small Target Defect Detection Method
Jingtao Zhang1, Siwen Chen1, Wei Wang2
1Department of Robotics Engineering, Nanjing University of Information Science and Technology, 219 Ningliu Road, Pukou District, Nanjing 210044, China.
Sensors (Basel, Switzerland)
|October 29, 2025
Summary
This study introduces TDD-YOLO, an optimized drone-based system for automated distribution tower inspection. It significantly improves the detection of small defects in real-time, enhancing inspection efficiency and safety.
Area of Science:
- Engineering
- Computer Science
- Artificial Intelligence
Background:
- Automated distribution tower inspection using drones faces challenges in real-time defect detection.
- Limited computational power and the small size of critical defects hinder current automated systems.
Purpose of the Study:
- To propose TDD-YOLO, an optimized model for enhanced small defect detection in drone-based distribution tower inspection.
- To improve the accuracy and real-time performance of automated defect identification.
Main Methods:
- Developed TDD-YOLO, an optimized model based on YOLOv11n, incorporating SPD-Conv, CBAM, BiFPN, and a high-resolution detection head.
- Evaluated the model on a custom dataset for defect detection.
- Deployed the system on a Jetson Orin Nano for real-time performance testing.
Main Results:
- TDD-YOLO achieved a mean Average Precision (mAP@0.5) of 0.873, outperforming the baseline by 3.9%.
- The system demonstrated a practical frame rate of 28 FPS on a Jetson Orin Nano at 640 × 640 resolution.
- The proposed enhancements effectively improved small defect detection and localization precision.
Conclusions:
- TDD-YOLO offers a viable solution for real-time, automated defect detection in distribution tower inspections.
- The optimized model significantly enhances the capabilities of drones for infrastructure monitoring.
- The system's performance indicates practical applicability for autonomous inspection tasks.
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