Road Surface Defect Detection-From Image-Based to Non-Image-Based: A Survey
Ensuring traffic safety is crucial, which necessitates the detection and prevention of road surface defects. As a result, there has been a growing interest in the literature on the subject, leading to the development of various road surface defect detection methods. The methods for detecting road de...
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| Published in | IEEE transactions on intelligent transportation systems Vol. 25; no. 9; pp. 10581 - 10603 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.09.2024
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Ensuring traffic safety is crucial, which necessitates the detection and prevention of road surface defects. As a result, there has been a growing interest in the literature on the subject, leading to the development of various road surface defect detection methods. The methods for detecting road defects can be categorised in various ways depending on the input data types or training methodologies. The predominant approach involves image-based methods, which analyse pixel intensities and surface textures to identify defects. Despite popularity, image-based methods share the distinct limitation of vulnerability to weather and lighting changes. To address this issue, researchers have explored the use of additional sensors, such as laser scanners or LiDARs, providing explicit depth information to enable the detection of defects in terms of scale and volume. However, the exploration of data beyond images has not been sufficiently investigated. In this survey paper, we provide a comprehensive review of road surface defect detection studies, categorising them based on input data types and methodologies used. Additionally, we review recently proposed non-image-based methods and discuss several challenges and open problems associated with these techniques. |
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| AbstractList | Ensuring traffic safety is crucial, which necessitates the detection and prevention of road surface defects. As a result, there has been a growing interest in the literature on the subject, leading to the development of various road surface defect detection methods. The methods for detecting road defects can be categorised in various ways depending on the input data types or training methodologies. The predominant approach involves image-based methods, which analyse pixel intensities and surface textures to identify defects. Despite popularity, image-based methods share the distinct limitation of vulnerability to weather and lighting changes. To address this issue, researchers have explored the use of additional sensors, such as laser scanners or LiDARs, providing explicit depth information to enable the detection of defects in terms of scale and volume. However, the exploration of data beyond images has not been sufficiently investigated. In this survey paper, we provide a comprehensive review of road surface defect detection studies, categorising them based on input data types and methodologies used. Additionally, we review recently proposed non-image-based methods and discuss several challenges and open problems associated with these techniques. |
| Author | Luo, Shan Fichera, Sebastiano Layzell, Lisa Yu, Jongmin Paoletti, Paolo Jiang, Jiaqi Mehta, Devansh |
| Author_xml | – sequence: 1 givenname: Jongmin orcidid: 0000-0002-0718-9948 surname: Yu fullname: Yu, Jongmin email: bartosz.regula@gmail.com organization: Department of Engineering, King's College London, London, U.K – sequence: 2 givenname: Jiaqi orcidid: 0000-0001-8366-5750 surname: Jiang fullname: Jiang, Jiaqi email: jiaqi.1.jiang@kcl.ac.uk organization: Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, U.K – sequence: 3 givenname: Sebastiano orcidid: 0000-0003-1006-4959 surname: Fichera fullname: Fichera, Sebastiano email: Sebastiano.Fichera@liverpool.ac.uk organization: Department of Engineering, King's College London, London, U.K – sequence: 4 givenname: Paolo orcidid: 0000-0001-6131-0377 surname: Paoletti fullname: Paoletti, Paolo email: paoletti@liverpool.ac.uk organization: Department of Engineering, King's College London, London, U.K – sequence: 5 givenname: Lisa surname: Layzell fullname: Layzell, Lisa email: lisa.layzell@robotiz3d.com organization: Robotiz3d Ltd., Daresbury, U.K – sequence: 6 givenname: Devansh surname: Mehta fullname: Mehta, Devansh email: devansh.mehta@robotiz3d.com organization: Robotiz3d Ltd., Daresbury, U.K – sequence: 7 givenname: Shan orcidid: 0000-0003-4760-0372 surname: Luo fullname: Luo, Shan email: shan.luo@kcl.ac.uk organization: Department of Engineering, King's College London, London, U.K |
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| Snippet | Ensuring traffic safety is crucial, which necessitates the detection and prevention of road surface defects. As a result, there has been a growing interest in... |
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| SubjectTerms | Asphalt crack detection deep learning Defect detection object detection object segmentation Road surface defect detection Roads Sensors Soil Surface cracks Surveys |
| Title | Road Surface Defect Detection-From Image-Based to Non-Image-Based: A Survey |
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