Enhanced YOLOv8-based pavement crack detection: A high-precision approach
At present, the repair of cracks is still implemented manually, which has the problems of low identification efficiency and high labor cost. Crack detection is the key to realize the mechanical and intelligent crack repair. To solve these problems, an improved automatic recognition algorithm based o...
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| Vydané v: | PloS one Ročník 20; číslo 5; s. e0324512 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
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22.05.2025
Public Library of Science (PLoS) |
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| Abstract | At present, the repair of cracks is still implemented manually, which has the problems of low identification efficiency and high labor cost. Crack detection is the key to realize the mechanical and intelligent crack repair. To solve these problems, an improved automatic recognition algorithm based on YOLOv8 model, YOLOV8-DGS is proposed in this study. Firstly, this paper introduces deep separable Convolution (DWConv) into YOLOv8 backbone network to capture crack information more flexibly and improve the recognition accuracy of the model. Secondly, GSConv is used in the neck part to reduce computation and enhance feature representation, especially in the processing of multi-scale fracture features. Through these improvements, YOLOv8-DGS not only improves the accuracy of small cracks, but also ensures the real-time and high efficiency of intelligent joint filling equipment in practical applications. Experimental results show that the Precision, Recall, F1-score, mAP50 and FPS of the YOLOv8-DGS algorithm in pavement crack detection are 91.6%, 90%, 90.8%, 92.4% and 85 frames, respectively. At the same time, the recognition rate of different types of cracks in the model reached more than 86%, which increased by 20.5% compared with the YOLO11 model. This method can provide theoretical basis for automatic crack identification and technical support for automatic seam filling machine. |
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| AbstractList | At present, the repair of cracks is still implemented manually, which has the problems of low identification efficiency and high labor cost. Crack detection is the key to realize the mechanical and intelligent crack repair. To solve these problems, an improved automatic recognition algorithm based on YOLOv8 model, YOLOV8-DGS is proposed in this study. Firstly, this paper introduces deep separable Convolution (DWConv) into YOLOv8 backbone network to capture crack information more flexibly and improve the recognition accuracy of the model. Secondly, GSConv is used in the neck part to reduce computation and enhance feature representation, especially in the processing of multi-scale fracture features. Through these improvements, YOLOv8-DGS not only improves the accuracy of small cracks, but also ensures the real-time and high efficiency of intelligent joint filling equipment in practical applications. Experimental results show that the Precision, Recall, F1-score, mAP50 and FPS of the YOLOv8-DGS algorithm in pavement crack detection are 91.6%, 90%, 90.8%, 92.4% and 85 frames, respectively. At the same time, the recognition rate of different types of cracks in the model reached more than 86%, which increased by 20.5% compared with the YOLO11 model. This method can provide theoretical basis for automatic crack identification and technical support for automatic seam filling machine. At present, the repair of cracks is still implemented manually, which has the problems of low identification efficiency and high labor cost. Crack detection is the key to realize the mechanical and intelligent crack repair. To solve these problems, an improved automatic recognition algorithm based on YOLOv8 model, YOLOV8-DGS is proposed in this study. Firstly, this paper introduces deep separable Convolution (DWConv) into YOLOv8 backbone network to capture crack information more flexibly and improve the recognition accuracy of the model. Secondly, GSConv is used in the neck part to reduce computation and enhance feature representation, especially in the processing of multi-scale fracture features. Through these improvements, YOLOv8-DGS not only improves the accuracy of small cracks, but also ensures the real-time and high efficiency of intelligent joint filling equipment in practical applications. Experimental results show that the Precision, Recall, F1-score, mAP50 and FPS of the YOLOv8-DGS algorithm in pavement crack detection are 91.6%, 90%, 90.8%, 92.4% and 85 frames, respectively. At the same time, the recognition rate of different types of cracks in the model reached more than 86%, which increased by 20.5% compared with the YOLO11 model. This method can provide theoretical basis for automatic crack identification and technical support for automatic seam filling machine.At present, the repair of cracks is still implemented manually, which has the problems of low identification efficiency and high labor cost. Crack detection is the key to realize the mechanical and intelligent crack repair. To solve these problems, an improved automatic recognition algorithm based on YOLOv8 model, YOLOV8-DGS is proposed in this study. Firstly, this paper introduces deep separable Convolution (DWConv) into YOLOv8 backbone network to capture crack information more flexibly and improve the recognition accuracy of the model. Secondly, GSConv is used in the neck part to reduce computation and enhance feature representation, especially in the processing of multi-scale fracture features. Through these improvements, YOLOv8-DGS not only improves the accuracy of small cracks, but also ensures the real-time and high efficiency of intelligent joint filling equipment in practical applications. Experimental results show that the Precision, Recall, F1-score, mAP50 and FPS of the YOLOv8-DGS algorithm in pavement crack detection are 91.6%, 90%, 90.8%, 92.4% and 85 frames, respectively. At the same time, the recognition rate of different types of cracks in the model reached more than 86%, which increased by 20.5% compared with the YOLO11 model. This method can provide theoretical basis for automatic crack identification and technical support for automatic seam filling machine. |
| Audience | Academic |
| Author | Zhang, ZuXuan Zhang, TongJia Zhang, HongLi |
| AuthorAffiliation | 1 School of Engineering Machinery, Shandong Jiaotong University, Jinan, China 2 School of Mechanical and Electrical Engineering, Shandong Jianzhu University, Jinan, China Jouf University, SAUDI ARABIA |
| AuthorAffiliation_xml | – name: 2 School of Mechanical and Electrical Engineering, Shandong Jianzhu University, Jinan, China – name: 1 School of Engineering Machinery, Shandong Jiaotong University, Jinan, China – name: Jouf University, SAUDI ARABIA |
| Author_xml | – sequence: 1 givenname: ZuXuan surname: Zhang fullname: Zhang, ZuXuan – sequence: 2 givenname: HongLi orcidid: 0009-0007-8150-1913 surname: Zhang fullname: Zhang, HongLi – sequence: 3 givenname: TongJia surname: Zhang fullname: Zhang, TongJia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40403041$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.engappai.2023.106217 10.1016/j.oceaneng.2022.111735 10.1016/j.infrared.2023.104894 10.1109/ICCV48922.2021.00376 10.1016/j.comnet.2024.110656 10.3390/rs15174251 10.1016/j.autcon.2023.105062 10.3390/rs15102663 10.1109/TVT.2024.3492388 10.1038/s41598-024-66234-3 10.1016/j.ssci.2024.106690 10.1007/s12008-024-01769-3 10.1109/TITS.2020.2990703 10.3390/jmse12101748 10.3390/s24144491 10.1016/j.autcon.2021.103973 |
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| Copyright | Copyright: © 2025 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2025 Public Library of Science 2025 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2025 Zhang et al 2025 Zhang et al 2025 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Snippet | At present, the repair of cracks is still implemented manually, which has the problems of low identification efficiency and high labor cost. Crack detection is... |
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| SubjectTerms | Accuracy Algorithms Asphalt pavements Biology and Life Sciences Computer and Information Sciences Computer vision Construction Materials Cracking Cracks Datasets Deep learning Engineering and Technology Neural networks Pavements Physical Sciences Real time Recognition Repair Research and Analysis Methods Sewing machines Social Sciences |
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| Title | Enhanced YOLOv8-based pavement crack detection: A high-precision approach |
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