Journal of Applied Science and Engineering (Apr 2025)

Research on surface loss detection algorithm of wind turbine blade based on FRE-DETR network

  • Jingwei Yang,
  • Xiaocong Chen,
  • Shengxian Cao,
  • Bo Zhao,
  • Zhenhao Tang,
  • Gong Wang,
  • Xingyu Li,
  • Han Gao

DOI
https://doi.org/10.6180/jase.202512_28(12).0002
Journal volume & issue
Vol. 28, no. 12
pp. 2329 – 2339

Abstract

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Wind turbine generators operate in harsh areas for a long time, resulting in frequent problems such as blade breakage, and traditional blade defect detection methods have low detection accuracy. In this paper, an end-toend target detection algorithm FRE-DETR based on wind turbine blade defects is designed, and the detection speed and detection accuracy of the end-to-end detection model are further improved by redesigning the feature extraction location in the backbone network and proposing a feature selection and fusion module. FRE-DETR is tested on a wind turbine blade defect dataset, and the results show that the model improves the detection accuracy by 2% compared with RTDETR-R18. The inference speed is already higher than RTDETR-R18 when the step size is larger than 2. The Gflops of the model is only 66.8% of that of RTDETR-R18, which also greatly reduces the computational requirements of the hardware when deployed. FRE-DETR meets the requirements of real-time detection.

Keywords