Automatic visual defects inspection of wind turbine blades via YOLO-based small object detection approach

Abstract

Regular inspection of wind turbine blades (WTBs), especially the detection of tiny defects, is necessary to maintain safe operation of wind turbine systems. However, current detections are inefficient and subjective because they are conducted merely by human inspectors. An autonomous visual inspection system is proposed in this paper for WTBs, in which a deep learning framework is developed by combining the convolutional neural network (CNN) and the you only look once (YOLO) model. To achieve practically acceptable detection accuracy for small-sized defects on the WTBs, a YOLO-based small object detection approach (YSODA) using a multiscale feature pyramid is proposed by amalgamating features of more layers. To evaluate the proposed YSODA, a database including 23,807 images labeled for three types of defect—crack, oil pollution, and sand inclusion, is developed. Then, the YSODA is with its architecture modified, and is trained, validated, and tested using the images from the database to provide autonomous and accurate visual inspection. After training and testing, resulting detection accuracy reaches 92.7%, 90.7%, and 90.3% for the three types of defect with the average accuracy being 91.3%. The robustness of the trained YSODA is demonstrated and verified in detecting small-sized defects. It is also compared with that of the traditional CNN-based and machine learning methods by applying to a real WTB system, which proved that the proposed YSODA is superior to existing approaches in terms of detection accuracy and reliability.