Research on surface loss detection algorithm of wind turbine blade based on FRE-DETR network
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 de...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Tamkang University Press
2025-04-01
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| Series: | Journal of Applied Science and Engineering |
| Subjects: | |
| Online Access: | http://jase.tku.edu.tw/articles/jase-202512-28-12-0002 |
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| _version_ | 1849713712098705408 |
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| author | Jingwei Yang Xiaocong Chen Shengxian Cao Bo Zhao Zhenhao Tang Gong Wang Xingyu Li Han Gao |
| author_facet | Jingwei Yang Xiaocong Chen Shengxian Cao Bo Zhao Zhenhao Tang Gong Wang Xingyu Li Han Gao |
| author_sort | Jingwei Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a382e56fc0d74a99a8b281fb58a7da23 |
| institution | DOAJ |
| issn | 2708-9967 2708-9975 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj-art-a382e56fc0d74a99a8b281fb58a7da232025-08-20T03:13:54ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752025-04-0128122329233910.6180/jase.202512_28(12).0002Research on surface loss detection algorithm of wind turbine blade based on FRE-DETR networkJingwei Yang0Xiaocong Chen1Shengxian Cao2Bo Zhao3Zhenhao Tang4Gong Wang5Xingyu Li6Han Gao7Zhongdian Huachuang Electric Power Technology Research Co., Ltd., 215123, Suzhou, China.Hubei Zhongdian Chunyangshan wind power Co., LTD., 430040, Wuhan, China.Northeast Electric Power University, 132012, Jilin, China.Northeast Electric Power University, 132012, Jilin, China.Northeast Electric Power University, 132012, Jilin, China.Northeast Electric Power University, 132012, Jilin, China.Northeast Electric Power University, 132012, Jilin, China.Northeast Electric Power University, 132012, Jilin, China.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.http://jase.tku.edu.tw/articles/jase-202512-28-12-0002integrated energy systemend-to-end algorithmwind turbine blade defecttarget detect; computer vision |
| spellingShingle | Jingwei Yang Xiaocong Chen Shengxian Cao Bo Zhao Zhenhao Tang Gong Wang Xingyu Li Han Gao Research on surface loss detection algorithm of wind turbine blade based on FRE-DETR network Journal of Applied Science and Engineering integrated energy system end-to-end algorithm wind turbine blade defect target detect; computer vision |
| title | Research on surface loss detection algorithm of wind turbine blade based on FRE-DETR network |
| title_full | Research on surface loss detection algorithm of wind turbine blade based on FRE-DETR network |
| title_fullStr | Research on surface loss detection algorithm of wind turbine blade based on FRE-DETR network |
| title_full_unstemmed | Research on surface loss detection algorithm of wind turbine blade based on FRE-DETR network |
| title_short | Research on surface loss detection algorithm of wind turbine blade based on FRE-DETR network |
| title_sort | research on surface loss detection algorithm of wind turbine blade based on fre detr network |
| topic | integrated energy system end-to-end algorithm wind turbine blade defect target detect; computer vision |
| url | http://jase.tku.edu.tw/articles/jase-202512-28-12-0002 |
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