Research on lightweighting of wind turbine blade surface defect detection algorithm

doi:10.13295/j.cnki.issn1673-5196.2026.04.006

Abstract

Abstract: Given the low accuracy and poor efficiency of manual and traditional automated detection algorithms for identifying surface cracks and exfoliation corrosion defects in wind turbine blades, a lightweight algorithm based on optimized YOLOv5s for surface defect detection in wind turbine blades has been proposed. This algorithm involves the creation of a dataset encompassing blade surface defects observed under various natural conditions. Ghost modules and Ghost Bottleneck structures from the GhostNet are integrated into YOLOv5s to enhance its capabilities. Soft Intersection over Union (SIoU) is introduced to improve prediction regression, with a focus on standard quality anchor boxes, bridging the gap between actual and predicted bounding boxes. This enhancement significantly boosts the accuracy of blade surface defect recognition and improves localization precision. Furthermore, the optimized model reduces parameters, computational load, and the weight file size of YOLOv5s by 52.6%, 51.6%, and 55.2%, respectively. The algorithm achieves an average precision of 88.3% in recognizing blade surface defects. Comparative analysis against Faster-RCNN, SSD, YOLOv4, YOLOv5s, and YOLOv8s algorithms reveals improvements in average precision by 34.2%, 36.5%, 24.5%, 1.9%, and 1.8%, respectively. The experimental results indicate that the algorithm successfully prevents both missed detections and false positives after optimization, while also improving the accuracy of target localization, making it more suitable for detecting surface cracks and exfoliation corrosion defects on blades under complex working conditions.

Key words: wind turbine blade, image recognition, YOLOv5s, object detection, lightweight algorithm

CLC Number:

TK83
TP206.3

WEI Tai, LIU Yu-hang, XUE Wen-jie, CHEN Xue-you. Research on lightweighting of wind turbine blade surface defect detection algorithm[J]. Journal of Lanzhou University of Technology, 2026, 52(4): 52-58.

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URL: https://journal.lut.edu.cn/EN/10.13295/j.cnki.issn1673-5196.2026.04.006

https://journal.lut.edu.cn/EN/Y2026/V52/I4/52

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