Efficient and Accurate Damage Detector for Wind Turbine Blade Images
The damage of wind turbine blades is one of the main problems restricting wind power development. Object detection can identify the damaged regions and diagnose the damage types. To handle the high-resolution wind turbine blade images, this article presents a novel efficient, and accurate damage det...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9963556/ |
| _version_ | 1797987486784290816 |
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| author | Liang Lv Zhongyuan Yao Enming Wang Xin Ren Ran Pang Hua Wang Yu Zhang Hao Wu |
| author_facet | Liang Lv Zhongyuan Yao Enming Wang Xin Ren Ran Pang Hua Wang Yu Zhang Hao Wu |
| author_sort | Liang Lv |
| collection | DOAJ |
| description | The damage of wind turbine blades is one of the main problems restricting wind power development. Object detection can identify the damaged regions and diagnose the damage types. To handle the high-resolution wind turbine blade images, this article presents a novel efficient, and accurate damage detector (EADD) for wind turbine blade images. The proposed method adopts Single Shot MultiBox Detector (SSD) as the detection framework and offers an improved ResNet as the backbone. Firstly, the improved ResNet backbone uses dense connection blocks consisting of factorized depth-wise separable bottleneck (FDSB) and feature aggregation module (FAM), which makes the damage detection model more lightweight and has a faster detection speed. Secondly, the bidirectional cross-scale feature pyramid (BiFPN) is introduced into the proposed method to use multi-scale features fully and have more feature expression. In addition, data pre-processing, exponential moving average (EMA) and label smooth methods are utilized to improve the accuracy and robustness of the model. The experimental results on the wind turbine blade damage detection dataset show that our proposed method can achieve the best trade-off between detection accuracy and computation time compared with other competitive methods. |
| first_indexed | 2024-04-11T07:49:13Z |
| format | Article |
| id | doaj.art-32af8745e8224c11905f54b026c4fc72 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T07:49:13Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-32af8745e8224c11905f54b026c4fc722022-12-22T04:36:09ZengIEEEIEEE Access2169-35362022-01-011012337812338610.1109/ACCESS.2022.32244469963556Efficient and Accurate Damage Detector for Wind Turbine Blade ImagesLiang Lv0https://orcid.org/0000-0003-4083-394XZhongyuan Yao1Enming Wang2Xin Ren3Ran Pang4Hua Wang5Yu Zhang6Hao Wu7https://orcid.org/0000-0001-7184-6802China Huaneng Clean Energy Research Institute, Beijing, ChinaHuaneng Yancheng Dafeng New Energy Power Generation Company Ltd., Nanjing, ChinaChina Huaneng Clean Energy Research Institute, Beijing, ChinaChina Huaneng Clean Energy Research Institute, Beijing, ChinaHuaneng Yancheng Dafeng New Energy Power Generation Company Ltd., Nanjing, ChinaChina Huaneng Clean Energy Research Institute, Beijing, ChinaHuaneng Yancheng Dafeng New Energy Power Generation Company Ltd., Nanjing, ChinaChina Huaneng Clean Energy Research Institute, Beijing, ChinaThe damage of wind turbine blades is one of the main problems restricting wind power development. Object detection can identify the damaged regions and diagnose the damage types. To handle the high-resolution wind turbine blade images, this article presents a novel efficient, and accurate damage detector (EADD) for wind turbine blade images. The proposed method adopts Single Shot MultiBox Detector (SSD) as the detection framework and offers an improved ResNet as the backbone. Firstly, the improved ResNet backbone uses dense connection blocks consisting of factorized depth-wise separable bottleneck (FDSB) and feature aggregation module (FAM), which makes the damage detection model more lightweight and has a faster detection speed. Secondly, the bidirectional cross-scale feature pyramid (BiFPN) is introduced into the proposed method to use multi-scale features fully and have more feature expression. In addition, data pre-processing, exponential moving average (EMA) and label smooth methods are utilized to improve the accuracy and robustness of the model. The experimental results on the wind turbine blade damage detection dataset show that our proposed method can achieve the best trade-off between detection accuracy and computation time compared with other competitive methods.https://ieeexplore.ieee.org/document/9963556/Wind turbine bladedamage detectionSSDdense connectionBiFPN |
| spellingShingle | Liang Lv Zhongyuan Yao Enming Wang Xin Ren Ran Pang Hua Wang Yu Zhang Hao Wu Efficient and Accurate Damage Detector for Wind Turbine Blade Images IEEE Access Wind turbine blade damage detection SSD dense connection BiFPN |
| title | Efficient and Accurate Damage Detector for Wind Turbine Blade Images |
| title_full | Efficient and Accurate Damage Detector for Wind Turbine Blade Images |
| title_fullStr | Efficient and Accurate Damage Detector for Wind Turbine Blade Images |
| title_full_unstemmed | Efficient and Accurate Damage Detector for Wind Turbine Blade Images |
| title_short | Efficient and Accurate Damage Detector for Wind Turbine Blade Images |
| title_sort | efficient and accurate damage detector for wind turbine blade images |
| topic | Wind turbine blade damage detection SSD dense connection BiFPN |
| url | https://ieeexplore.ieee.org/document/9963556/ |
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