Automated Wind Turbine Inspection and Structural Damage Detection Using YOLOv5s

An automated damage detection method based on deep learning for wind turbine blades uses the YOLOv5 framework, which is object detection that is quite fast and relatively accurate for detecting objects and localization instances of damage in images. Source data has been a curated collection dataset of wind turbine images with annotated regions of damage; thus, a model was trained from Roboflow. This uses transfer learning in which the model is started with pre-trained YOLOv5s weights and further fine-tunes it over the specific dataset. It also fits well into the nature of the data-set including the number of classes of damage. The training process, therefore, is optimizing the model parameters to achieve the minimization of differences between predicted bounding boxes and the available ground-truth annotations. Standard metrics for object detection are used in the evaluation of the model: precision, recall, mean Average Precision (mAP), and F1-score. Qualitative assessment is also carried through as a visual check on the predictions that the model would produce on unseen images, validating its use in real conditions. It contributes to developing an automated and efficient system concerning structural health monitoring of wind turbines and enables maintenance at the appropriate time, thus decreasing down times.