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Open Access

ARTICLE

Defect Detection of Wind Turbine Blades Using Multiscale Feature Extraction and Attention Mechanism

Yajuan Lu*, Yongtao Hu, Jie Li, Jinping Zhang, Jingjing Si
School of Electrical Engineering and Automation, Henan Institute of Technology, Xinxiang, 453003, China
* Corresponding Author: Yajuan Lu. Email: email
(This article belongs to the Special Issue: Artificial Intelligence and Data Mining Applications in Fault Diagnosis and Damage Identification of Infrastructure)

Structural Durability & Health Monitoring https://doi.org/10.32604/sdhm.2025.071110

Received 31 July 2025; Accepted 04 November 2025; Published online 28 November 2025

Abstract

To address challenges in wind turbine blade defect detection models, primarily due to insufficient feature extraction capabilities and the difficulty of deploying models on drone-type edge devices, this study proposes a wind turbine blade defect detection model, WtCS-YOLO11, that incorporates multiscale feature extraction and an attention mechanism. Firstly, the cross-stage partial with two kernels and a wavelet convolution module (C3k2_WTConv) is proposed by introducing wavelet convolution into the module. The cross-stage partial with two kernels (C3k2) module in the necking network is replaced with the C3k2_WTConv module to increase the model’s receptive field, enable multiscale feature extraction, and reduce computational parameter usage. Second, the convolutional block attention module (CBAM) is proposed and applied to the neck network, integrating channel and spatial attention, allowing the model to focus on essential features and enhance its ability to detect large targets. In addition, the model employs shape-aware intersection over union (Shape-IoU), which focuses on the shape and scale of bounding boxes, and combines the normalized Wasserstein distance (NWD) to calculate bounding box similarity, thereby improving the accuracy of bounding-box regression. In this study, a dataset for wind turbine blade defect detection was constructed covering six defect categories. The experimental results showed that the precision (P), recall (R), and mean average precision at the intersection over union threshold of 0.5 (mAP50) for the WtCS-YOLO11 model were 84.4%, 86.9%, and 89.7%, respectively. Compared to the baseline You Only Look Once 11 (YOLO11) model, P, R, and mAP50 improved by 5.9%, 2.5%, and 2.4%, respectively, with virtually no increase in computational complexity or parameter count. WtCS-YOLO11 improved the precision measurement accuracy. Its model size and computational complexity are suitable for deployment on edge devices, and it achieves high inference speed, meeting the application requirements for real-time wind turbine blade defect detection.

Keywords

Wind turbine blade defect detection; wavelet convolution; YOLO11; object detection

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