LAPSE:2023.35712
Published Article
LAPSE:2023.35712
WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX
Yuan Yao, Guozhong Wang, Jinhui Fan
May 23, 2023
Wind turbine blades will suffer various surface damages due to their operating environment and high-speed rotation. Accurate identification in the early stage of damage formation is crucial. The damage detection of wind turbine blades is a primarily manual operation, which has problems such as high cost, low efficiency, intense subjectivity, and high risk. The rise of deep learning provides a new method for detecting wind turbine blade damage. However, in detecting wind turbine blade damage in general network models, there will be an insufficient fusion of multiscale small target features. This paper proposes a lightweight cascaded feature fusion neural network model based on YOLOX. Firstly, the lightweight area of the backbone feature extraction network concerning the RepVGG network structure is enhanced, improving the model’s inference speed. Second, a cascaded feature fusion module is designed to cascade and interactively fuse multilevel features to enhance the small target area features and the model’s feature perception capabilities for multiscale target damage. The focal loss is introduced in the post-processing stage to enhance the network’s ability to learn complex positive sample damages. The detection accuracy of the improved algorithm is increased by 2.95%, the mAP can reach 94.29% in the self-made dataset, and the recall rate and detection speed are slightly improved. The experimental results show that the algorithm can autonomously learn the blade damage features from the wind turbine blade images collected in the actual scene, achieve the automatic detection, location, and classification of wind turbine blade damage, and promote the detection of wind turbine blade damage towards automation, rapidity, and low-cost development.
Keywords
cascaded feature fusion, focal loss, object detection, RepVGG, YOLO
Suggested Citation
Yao Y, Wang G, Fan J. WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX. (2023). LAPSE:2023.35712
Author Affiliations
Yao Y: School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
Wang G: School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
Fan J: School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China; Shanghai Media Group, Shanghai 200041, China
Journal Name
Energies
Volume
16
Issue
9
First Page
3776
Year
2023
Publication Date
2023-04-28
Published Version
ISSN
1996-1073
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Original Submission
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PII: en16093776, Publication Type: Journal Article
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LAPSE:2023.35712
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doi:10.3390/en16093776
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May 23, 2023
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CC BY 4.0
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[v1] (Original Submission)
May 23, 2023
 
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May 23, 2023
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Original Submitter
Calvin Tsay
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