Wind Turbine Surface Crack Detection Based on YOLOv5l-GCB

Feng Hu, Xiaohui Leng, Chao Ma, Guoming Sun, Dehong Wang (), Duanxuan Liu and Zixuan Zhang
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Feng Hu: CGN New Energy Investment (Shenzhen) Co., Ltd. (Jilin Branch), Changchun 130028, China
Xiaohui Leng: CGN New Energy Investment (Shenzhen) Co., Ltd. (Jilin Branch), Changchun 130028, China
Chao Ma: CGN New Energy Investment (Shenzhen) Co., Ltd. (Jilin Branch), Changchun 130028, China
Guoming Sun: CGN New Energy Investment (Shenzhen) Co., Ltd. (Jilin Branch), Changchun 130028, China
Dehong Wang: School of Civil Engineering and Architecture, Northeast Electric Power University, Jilin 132012, China
Duanxuan Liu: School of Civil Engineering and Architecture, Northeast Electric Power University, Jilin 132012, China
Zixuan Zhang: School of Civil Engineering and Architecture, Northeast Electric Power University, Jilin 132012, China

Energies, 2025, vol. 18, issue 11, 1-15

Abstract: As a fundamental element of the wind power generation system, the timely detection and rectification of surface cracks and other defects are imperative to ensure the stable function of the entire system. A new wind tower surface crack detection model, You Only Look Once version 5l GhostNetV2-CBAM-BiFPN (YOLOv5l-GCB), is proposed to accomplish the accurate classification of wind tower surface cracks. Ghost Network Version 2 (GhostNetV2) is integrated into the backbone of YOLOv5l to realize lightweighting of the backbone, which simplifies the complexity of the model and enhances the inference speed; the Convolutional Block Attention Module (CBAM) is added to strengthen the attention of the model to the target region; and the bidirectional feature pyramid network (BiFPN) has been developed for the purpose of enhancing the model’s detection accuracy in complex scenes. The proposed improvement strategy is verified through ablation experiments. The experimental results indicate that the precision, recall, F1 score, and mean average precision of YOLOv5l-GCB reach 91.6%, 99.0%, 75.0%, and 84.6%, which are 4.7%, 2%, 1%, and 10.4% higher than that of YOLOv5l, and it can accurately recognize multiple types of cracks, with an average number of 28 images detected per second, which improves the detection speed.

Keywords: wind turbine tower; YOLO; target detection; concrete crack; CBAM; BiFPN; GhostNetV2 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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