WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/9/3776 |
| _version_ | 1827743071168626688 |
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| author | Yuan Yao Guozhong Wang Jinhui Fan |
| author_facet | Yuan Yao Guozhong Wang Jinhui Fan |
| author_sort | Yuan Yao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:20:45Z |
| format | Article |
| id | doaj.art-ff006ac34c2040fab26e7bdb32ae4b2e |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T04:20:45Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ff006ac34c2040fab26e7bdb32ae4b2e2023-11-17T22:51:35ZengMDPI AGEnergies1996-10732023-04-01169377610.3390/en16093776WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOXYuan Yao0Guozhong Wang1Jinhui Fan2School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaWind 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.https://www.mdpi.com/1996-1073/16/9/3776object detectionYOLORepVGGcascaded feature fusionfocal loss |
| spellingShingle | Yuan Yao Guozhong Wang Jinhui Fan WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX Energies object detection YOLO RepVGG cascaded feature fusion focal loss |
| title | WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX |
| title_full | WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX |
| title_fullStr | WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX |
| title_full_unstemmed | WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX |
| title_short | WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX |
| title_sort | wt yolox an efficient detection algorithm for wind turbine blade damage based on yolox |
| topic | object detection YOLO RepVGG cascaded feature fusion focal loss |
| url | https://www.mdpi.com/1996-1073/16/9/3776 |
| work_keys_str_mv | AT yuanyao wtyoloxanefficientdetectionalgorithmforwindturbinebladedamagebasedonyolox AT guozhongwang wtyoloxanefficientdetectionalgorithmforwindturbinebladedamagebasedonyolox AT jinhuifan wtyoloxanefficientdetectionalgorithmforwindturbinebladedamagebasedonyolox |