Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study
Pavement markings constitute an effective way of conveying regulations and guidance to drivers. They constitute the most fundamental way to communicate with road users, thus, greatly contributing to ensuring safety and order on roads. However, due to the increasingly extensive traffic demand, paveme...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/7/3152 |
| _version_ | 1797539122426937344 |
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| author | Shuyuan Xu Jun Wang Peng Wu Wenchi Shou Xiangyu Wang Mengcheng Chen |
| author_facet | Shuyuan Xu Jun Wang Peng Wu Wenchi Shou Xiangyu Wang Mengcheng Chen |
| author_sort | Shuyuan Xu |
| collection | DOAJ |
| description | Pavement markings constitute an effective way of conveying regulations and guidance to drivers. They constitute the most fundamental way to communicate with road users, thus, greatly contributing to ensuring safety and order on roads. However, due to the increasingly extensive traffic demand, pavement markings are subject to a series of deterioration issues (e.g., wear and tear). Markings in poor condition typically manifest as being blurred or even missing in certain places. The need for proper maintenance strategies on roadway markings, such as repainting, can only be determined based on a comprehensive understanding of their as-is worn condition. Given the fact that an efficient, automated and accurate approach to collect such condition information is lacking in practice, this study proposes a vision-based framework for pavement marking detection and condition assessment. A hybrid feature detector and a threshold-based method were used for line marking identification and classification. For each identified line marking, its worn/blurred severity level was then quantified in terms of worn percentage at a pixel level. The damage estimation results were compared to manual measurements for evaluation, indicating that the proposed method is capable of providing indicative knowledge about the as-is condition of pavement markings. This paper demonstrates the promising potential of computer vision in the infrastructure sector, in terms of implementing a wider range of managerial operations for roadway management. |
| first_indexed | 2024-03-10T12:40:47Z |
| format | Article |
| id | doaj.art-9da48292badf4284a8f8da7bdd8af0b9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T12:40:47Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9da48292badf4284a8f8da7bdd8af0b92023-11-21T13:54:34ZengMDPI AGApplied Sciences2076-34172021-04-01117315210.3390/app11073152Vision-Based Pavement Marking Detection and Condition Assessment—A Case StudyShuyuan Xu0Jun Wang1Peng Wu2Wenchi Shou3Xiangyu Wang4Mengcheng Chen5School of Design and the Built Environment, Curtin University, Perth, WA 6102, AustraliaSchool of Architecture and Built Environment, Deakin University, Melbourne, VIC 3220, AustraliaSchool of Design and the Built Environment, Curtin University, Perth, WA 6102, AustraliaSchool of Engineering, Design and Built Environment, Western Sydney University, Sydney, NSW 2115, AustraliaSchool of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang 330013, ChinaSchool of Civil Engineering and Architecture, East China Jiao Tong University, Nanchang 330013, ChinaPavement markings constitute an effective way of conveying regulations and guidance to drivers. They constitute the most fundamental way to communicate with road users, thus, greatly contributing to ensuring safety and order on roads. However, due to the increasingly extensive traffic demand, pavement markings are subject to a series of deterioration issues (e.g., wear and tear). Markings in poor condition typically manifest as being blurred or even missing in certain places. The need for proper maintenance strategies on roadway markings, such as repainting, can only be determined based on a comprehensive understanding of their as-is worn condition. Given the fact that an efficient, automated and accurate approach to collect such condition information is lacking in practice, this study proposes a vision-based framework for pavement marking detection and condition assessment. A hybrid feature detector and a threshold-based method were used for line marking identification and classification. For each identified line marking, its worn/blurred severity level was then quantified in terms of worn percentage at a pixel level. The damage estimation results were compared to manual measurements for evaluation, indicating that the proposed method is capable of providing indicative knowledge about the as-is condition of pavement markings. This paper demonstrates the promising potential of computer vision in the infrastructure sector, in terms of implementing a wider range of managerial operations for roadway management.https://www.mdpi.com/2076-3417/11/7/3152pavement managementline marking detectionaudible markingcondition assessmentcomputer vision |
| spellingShingle | Shuyuan Xu Jun Wang Peng Wu Wenchi Shou Xiangyu Wang Mengcheng Chen Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study Applied Sciences pavement management line marking detection audible marking condition assessment computer vision |
| title | Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study |
| title_full | Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study |
| title_fullStr | Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study |
| title_full_unstemmed | Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study |
| title_short | Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study |
| title_sort | vision based pavement marking detection and condition assessment a case study |
| topic | pavement management line marking detection audible marking condition assessment computer vision |
| url | https://www.mdpi.com/2076-3417/11/7/3152 |
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