Research on Asphalt Pavement Surface Distress Detection Technology Coupling Deep Learning and Object Detection Algorithms
To address the challenges posed by the vast scale of highway maintenance in China and the high costs associated with traditional inspection vehicles. This study focuses on a routine maintenance project for national and provincial roads in Shanxi Province, with an emphasis on the selection and design...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Infrastructures |
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| Online Access: | https://www.mdpi.com/2412-3811/10/4/72 |
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| author | Hong Zhang Yuanshuai Dong Yun Hou Xiangjun Cheng Peiwen Xie Keming Di |
| author_facet | Hong Zhang Yuanshuai Dong Yun Hou Xiangjun Cheng Peiwen Xie Keming Di |
| author_sort | Hong Zhang |
| collection | DOAJ |
| description | To address the challenges posed by the vast scale of highway maintenance in China and the high costs associated with traditional inspection vehicles. This study focuses on a routine maintenance project for national and provincial roads in Shanxi Province, with an emphasis on the selection and design of hardware for lightweight, portable pavement inspection devices. A monocular camera was used to capture pavement surface images, resulting in a dataset of 85,511 training samples. Additionally, the YOLOv5 object detection algorithm, combined with convolutional deep learning techniques, was employed to classify and identify pavement surface distresses in the collected images. Through multiple iterations of model tuning and validation, the proposed detection system achieved a false negative rate of 1.13%, a recall rate of 97.35%, and a precision rate of 98.30%. Its high accuracy provides a technical reference for the development and design of portable pavement distress detection devices. |
| format | Article |
| id | doaj-art-5d66b4a17d7c4ffa80c11f451ddf5942 |
| institution | DOAJ |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj-art-5d66b4a17d7c4ffa80c11f451ddf59422025-08-20T03:13:32ZengMDPI AGInfrastructures2412-38112025-03-011047210.3390/infrastructures10040072Research on Asphalt Pavement Surface Distress Detection Technology Coupling Deep Learning and Object Detection AlgorithmsHong Zhang0Yuanshuai Dong1Yun Hou2Xiangjun Cheng3Peiwen Xie4Keming Di5Department of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaDepartment of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaChina Highway Engineering Consulting Corporation, Beijing 100089, ChinaChina Highway Engineering Consulting Corporation, Beijing 100089, ChinaChina Highway Engineering Consulting Corporation DATA Co., Ltd., Beijing 100089, ChinaShanxi Provincial Highway Bureau Linfen Branch, Linfen 041000, ChinaTo address the challenges posed by the vast scale of highway maintenance in China and the high costs associated with traditional inspection vehicles. This study focuses on a routine maintenance project for national and provincial roads in Shanxi Province, with an emphasis on the selection and design of hardware for lightweight, portable pavement inspection devices. A monocular camera was used to capture pavement surface images, resulting in a dataset of 85,511 training samples. Additionally, the YOLOv5 object detection algorithm, combined with convolutional deep learning techniques, was employed to classify and identify pavement surface distresses in the collected images. Through multiple iterations of model tuning and validation, the proposed detection system achieved a false negative rate of 1.13%, a recall rate of 97.35%, and a precision rate of 98.30%. Its high accuracy provides a technical reference for the development and design of portable pavement distress detection devices.https://www.mdpi.com/2412-3811/10/4/72pavement engineeringasphalt pavement maintenancepavement surface inspectiondeep learningobject detection |
| spellingShingle | Hong Zhang Yuanshuai Dong Yun Hou Xiangjun Cheng Peiwen Xie Keming Di Research on Asphalt Pavement Surface Distress Detection Technology Coupling Deep Learning and Object Detection Algorithms Infrastructures pavement engineering asphalt pavement maintenance pavement surface inspection deep learning object detection |
| title | Research on Asphalt Pavement Surface Distress Detection Technology Coupling Deep Learning and Object Detection Algorithms |
| title_full | Research on Asphalt Pavement Surface Distress Detection Technology Coupling Deep Learning and Object Detection Algorithms |
| title_fullStr | Research on Asphalt Pavement Surface Distress Detection Technology Coupling Deep Learning and Object Detection Algorithms |
| title_full_unstemmed | Research on Asphalt Pavement Surface Distress Detection Technology Coupling Deep Learning and Object Detection Algorithms |
| title_short | Research on Asphalt Pavement Surface Distress Detection Technology Coupling Deep Learning and Object Detection Algorithms |
| title_sort | research on asphalt pavement surface distress detection technology coupling deep learning and object detection algorithms |
| topic | pavement engineering asphalt pavement maintenance pavement surface inspection deep learning object detection |
| url | https://www.mdpi.com/2412-3811/10/4/72 |
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