Thermal and RGB Images Work Better Together in Wind Turbine Damage Detection

TL;DR

This paper demonstrates that combining thermal and RGB images from UAVs significantly improves wind turbine blade defect detection accuracy and reduces false positives, enhancing maintenance efficiency.

Contribution

The study introduces a multispectral image composition method that effectively integrates thermal and RGB imagery for better defect detection on wind turbine blades.

Findings

YOLOv8 accuracy increased from 91% to 95%

False positives decreased from 6 to 3

Recall improved from 85% to 92%

Abstract

The inspection of wind turbine blades (WTBs) is crucial for ensuring their structural integrity and operational efficiency. Traditional inspection methods can be dangerous and inefficient, prompting the use of unmanned aerial vehicles (UAVs) that access hard-to-reach areas and capture high-resolution imagery. In this study, we address the challenge of enhancing defect detection on WTBs by integrating thermal and RGB images obtained from UAVs. We propose a multispectral image composition method that combines thermal and RGB imagery through spatial coordinate transformation, key point detection, binary descriptor creation, and weighted image overlay. Using a benchmark dataset of WTB images annotated for defects, we evaluated several state-of-the-art object detection models. Our results show that composite images significantly improve defect detection efficiency. Specifically, the YOLOv8 model's accuracy increased from 91% to 95%, precision from 89% to 94%, recall from 85% to 92%, and F1-score from 87% to 93%. The number of false positives decreased from 6 to 3, and missed defects reduced from 5 to 2. These findings demonstrate that integrating thermal and RGB imagery enhances defect detection on WTBs, contributing to improved maintenance and reliability.