Vision-based autonomous structural damage detection using data-driven methods
Seyyed Taghi Ataei, Parviz Mohammad Zadeh, Saeid Ataei
TL;DR
The study addresses automatic wind-turbine structural damage detection using vision-based SHM and compares YOLOv7, YOLOv7-Tiny, and Faster R-CNN on a labeled wind-turbine surface damage dataset. It demonstrates that YOLOv7 achieves a strong balance of accuracy and speed with mAP@50 of 82.4% and 11 ms per image, while YOLOv7-Tiny offers faster edge deployment and Faster R-CNN provides robustness for challenging, low-contrast damages but at slower speeds. The work highlights the potential of vision-based DL SHM to reduce maintenance costs, improve safety, and enable real-time inspections, contingent on larger, more diverse datasets and potential segmentation integration. It lays a foundation for scalable, automated SHM in wind-energy infrastructure and supports future exploration of edge deployment and segmentation-enabled monitoring.
Abstract
This study addresses the urgent need for efficient and accurate damage detection in wind turbine structures, a crucial component of renewable energy infrastructure. Traditional inspection methods, such as manual assessments and non-destructive testing (NDT), are often costly, time-consuming, and prone to human error. To tackle these challenges, this research investigates advanced deep learning algorithms for vision-based structural health monitoring (SHM). A dataset of wind turbine surface images, featuring various damage types and pollution, was prepared and augmented for enhanced model training. Three algorithms-YOLOv7, its lightweight variant, and Faster R-CNN- were employed to detect and classify surface damage. The models were trained and evaluated on a dataset split into training, testing, and evaluation subsets (80%-10%-10%). Results indicate that YOLOv7 outperformed the others, achieving 82.4% mAP@50 and high processing speed, making it suitable for real-time inspections. By optimizing hyperparameters like learning rate and batch size, the models' accuracy and efficiency improved further. YOLOv7 demonstrated significant advancements in detection precision and execution speed, especially for real-time applications. However, challenges such as dataset limitations and environmental variability were noted, suggesting future work on segmentation methods and larger datasets. This research underscores the potential of vision-based deep learning techniques to transform SHM practices by reducing costs, enhancing safety, and improving reliability, thus contributing to the sustainable maintenance of critical infrastructure and supporting the longevity of wind energy systems.