Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC
This study proposes a concrete dam underwater apparent defect detection algorithm named YOLOv8s-UEC for intelligent identification of underwater defects. Due to the scarcity of existing images of underwater concrete defects, this study establishes a dataset of underwater defect images by manually co...
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MDPI AG
2024-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/19/8731 |
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| author | Chenxi Liang Yang Zhao Fei Kang |
| author_facet | Chenxi Liang Yang Zhao Fei Kang |
| author_sort | Chenxi Liang |
| collection | DOAJ |
| description | This study proposes a concrete dam underwater apparent defect detection algorithm named YOLOv8s-UEC for intelligent identification of underwater defects. Due to the scarcity of existing images of underwater concrete defects, this study establishes a dataset of underwater defect images by manually constructing defective concrete walls for the training of defect detection networks. For the defect feature ambiguity that exists in underwater defects, the ConvNeXt Block module and Efficient-RepGFPN structure are introduced to enhance the feature extraction capability of the network, and the P2 detection layer is fused to enhance the detection capability of small-size defects such as cracks. The results show that the mean average precision (<i>mAP</i><sub>0.5</sub> and <i>mAP</i><sub>0.5:0.95</sub>) of the improved algorithm are increased by 1.4% and 5.8%, and it exhibits good robustness and considerable detection effect for underwater defects. |
| format | Article |
| id | doaj-art-3af47df646694062a23862b977b1fcd3 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3af47df646694062a23862b977b1fcd32025-08-20T01:47:41ZengMDPI AGApplied Sciences2076-34172024-09-011419873110.3390/app14198731Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UECChenxi Liang0Yang Zhao1Fei Kang2China Institute of Water Resources and Hydropower Research, Beijing 100048, ChinaSchool of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, ChinaThis study proposes a concrete dam underwater apparent defect detection algorithm named YOLOv8s-UEC for intelligent identification of underwater defects. Due to the scarcity of existing images of underwater concrete defects, this study establishes a dataset of underwater defect images by manually constructing defective concrete walls for the training of defect detection networks. For the defect feature ambiguity that exists in underwater defects, the ConvNeXt Block module and Efficient-RepGFPN structure are introduced to enhance the feature extraction capability of the network, and the P2 detection layer is fused to enhance the detection capability of small-size defects such as cracks. The results show that the mean average precision (<i>mAP</i><sub>0.5</sub> and <i>mAP</i><sub>0.5:0.95</sub>) of the improved algorithm are increased by 1.4% and 5.8%, and it exhibits good robustness and considerable detection effect for underwater defects.https://www.mdpi.com/2076-3417/14/19/8731concrete damsunderwater defectsdeep learningobject detectionmachine vision |
| spellingShingle | Chenxi Liang Yang Zhao Fei Kang Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC Applied Sciences concrete dams underwater defects deep learning object detection machine vision |
| title | Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC |
| title_full | Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC |
| title_fullStr | Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC |
| title_full_unstemmed | Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC |
| title_short | Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC |
| title_sort | intelligent detection of underwater defects in concrete dams based on yolov8s uec |
| topic | concrete dams underwater defects deep learning object detection machine vision |
| url | https://www.mdpi.com/2076-3417/14/19/8731 |
| work_keys_str_mv | AT chenxiliang intelligentdetectionofunderwaterdefectsinconcretedamsbasedonyolov8suec AT yangzhao intelligentdetectionofunderwaterdefectsinconcretedamsbasedonyolov8suec AT feikang intelligentdetectionofunderwaterdefectsinconcretedamsbasedonyolov8suec |