Abstract: In the wind power industry, the health state of wind turbine paddles is directly related to the power generation efficiency and the safe operation of the equipment. In order to solve the problems of low efficiency and insufficient accuracy of traditional detection methods, this paper proposes a wind turbine blade defect detection algorithm that integrates local channel attention and focus feature modulation. The algorithm first introduces the Mixed Local Channel Attention (MLCA) mechanism into the C2f module of the backbone network in YOLOv8 to enhance the extraction capability of the backbone network for key features. Then the Focal Feature Modulation (FFM) module is used to replace the original SPPF module in YOLOv8 to further aggregate global contextual features at different levels of granularity; finally, in the Neck part, the pro-gressive feature pyramid AFPN structure is used to enhance the multi-scale feature fusion capability of the model, which in turn improves the accuracy of small object detection. The experi-mental results show that the proposed algorithm has an accuracy of 82.5%, a mAP50 of 78.6%, and GFLOPS of 8.5. In the detection of wind turbine blade defects, which possesses higher detection performance and real-time performance compared with the traditional methods, and is able to effectively identify common defects such as cracks, corrosion, and abrasion, and exhibits strong robustness and application value.

Zheng Cao, Rundong He, Shaofei Zhang, Zhaoyang Qi, Sa Li, Tong Liu and Yue Li, “Integrating Local Channel Attention and Focused Feature Modulation for Wind Turbine Blade Defect Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151228

@article{Cao2024,
title = {Integrating Local Channel Attention and Focused Feature Modulation for Wind Turbine Blade Defect Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151228},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151228},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Zheng Cao and Rundong He and Shaofei Zhang and Zhaoyang Qi and Sa Li and Tong Liu and Yue Li}
}