Improving Wind Turbines Blades Damage Detection by Using YOLO BoF and BoS

In this study, we introduce a novel wind turbines blade's damage detection framework for reducing inspection time and improving accuracy in comparison with human. With recent developments and improvements in UAVs technology, aerial imagery can be used to achieve high resolution images from blades. The acquired images, can be analyzed by novel deep learning and image processing techniques like YOLO, to detect the failures and damages on surface of blades. But because of small training set and the structure of a damage in a image, we need to use advanced deep learning methods to achieve more accurate analysis than human eyes and also previous works. The proposed method is found to be effective based on experimental results in terms of suggesting damage locations and types on a surface, the system can achieve a level of precision better than a human-level inspection.