DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine Blades
Wind turbine blades (WTBs) are prone to damage from their working environment, including surface peeling and cracks. Early and effective detection of surface defects on WTBs can avoid complex and costly repairs and serious safety hazards. Traditional object detection methods have disadvantages of in...
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MDPI AG
2024-09-01
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| author | Li Zou Anqi Chen Chunzi Li Xinhua Yang Yibo Sun |
| author_facet | Li Zou Anqi Chen Chunzi Li Xinhua Yang Yibo Sun |
| author_sort | Li Zou |
| collection | DOAJ |
| description | Wind turbine blades (WTBs) are prone to damage from their working environment, including surface peeling and cracks. Early and effective detection of surface defects on WTBs can avoid complex and costly repairs and serious safety hazards. Traditional object detection methods have disadvantages of insufficient detection capabilities, extended model inference times, low recognition accuracy for small objects, and elongated strip defects within WTB datasets. In light of these challenges, a novel model named DCW-YOLO for surface damage detection of WTBs is proposed in this research, which leverages image data collected by unmanned aerial vehicles (UAVs) and the YOLOv8 algorithm for image analysis. Firstly, Dynamic Separable Convolution (DSConv) is introduced into the C2f module of YOLOv8, allowing the model to more effectively focus on the geometric structural details associated with damage on WTBs. Secondly, the upsampling method is replaced with the content-aware reassembly of features (CARAFE), which significantly minimizes the degradation of image characteristics throughout the upsampling process and boosts the network’s ability to extract features. Finally, the loss function is substituted with the WIoU (Wise-IoU) strategy. This strategy allows for a more accurate regression of the target bounding boxes and helps to improve the reliability in the localization of WTBs damages, especially for low-quality examples. This model demonstrates a notable superiority in surface damage detection of WTBs compared to the original YOLOv8n and has achieved a substantial improvement in the mAP@0.5 metric, rising from 91.4% to 93.8%. Furthermore, in the more rigorous mAP@0.5–0.95 metric, it has also seen an increase from 68.9% to 71.2%. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-09-01 |
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| spelling | doaj-art-e111d401bbab452494e55a0b036db7662025-08-20T01:47:44ZengMDPI AGApplied Sciences2076-34172024-09-011419876310.3390/app14198763DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine BladesLi Zou0Anqi Chen1Chunzi Li2Xinhua Yang3Yibo Sun4School of Intelligent Rail Engineering, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Intelligent Rail Engineering, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Chinese Language and Literature, Tangshan Normal University, Tangshan 063000, ChinaLiaoning Key Laboratory of Welding and Reliability of Rail Transportation Equipment, Dalian Jiaotong University, Dalian 116028, ChinaLiaoning Key Laboratory of Welding and Reliability of Rail Transportation Equipment, Dalian Jiaotong University, Dalian 116028, ChinaWind turbine blades (WTBs) are prone to damage from their working environment, including surface peeling and cracks. Early and effective detection of surface defects on WTBs can avoid complex and costly repairs and serious safety hazards. Traditional object detection methods have disadvantages of insufficient detection capabilities, extended model inference times, low recognition accuracy for small objects, and elongated strip defects within WTB datasets. In light of these challenges, a novel model named DCW-YOLO for surface damage detection of WTBs is proposed in this research, which leverages image data collected by unmanned aerial vehicles (UAVs) and the YOLOv8 algorithm for image analysis. Firstly, Dynamic Separable Convolution (DSConv) is introduced into the C2f module of YOLOv8, allowing the model to more effectively focus on the geometric structural details associated with damage on WTBs. Secondly, the upsampling method is replaced with the content-aware reassembly of features (CARAFE), which significantly minimizes the degradation of image characteristics throughout the upsampling process and boosts the network’s ability to extract features. Finally, the loss function is substituted with the WIoU (Wise-IoU) strategy. This strategy allows for a more accurate regression of the target bounding boxes and helps to improve the reliability in the localization of WTBs damages, especially for low-quality examples. This model demonstrates a notable superiority in surface damage detection of WTBs compared to the original YOLOv8n and has achieved a substantial improvement in the mAP@0.5 metric, rising from 91.4% to 93.8%. Furthermore, in the more rigorous mAP@0.5–0.95 metric, it has also seen an increase from 68.9% to 71.2%.https://www.mdpi.com/2076-3417/14/19/8763WTBsobject detectionYOLOloss function |
| spellingShingle | Li Zou Anqi Chen Chunzi Li Xinhua Yang Yibo Sun DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine Blades Applied Sciences WTBs object detection YOLO loss function |
| title | DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine Blades |
| title_full | DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine Blades |
| title_fullStr | DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine Blades |
| title_full_unstemmed | DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine Blades |
| title_short | DCW-YOLO: An Improved Method for Surface Damage Detection of Wind Turbine Blades |
| title_sort | dcw yolo an improved method for surface damage detection of wind turbine blades |
| topic | WTBs object detection YOLO loss function |
| url | https://www.mdpi.com/2076-3417/14/19/8763 |
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