Low-Cost and Contactless Survey Technique for Rapid Pavement Texture Assessment Using Mobile Phone Imagery

Collecting pavement texture information is crucial to understand the characteristics of a road surface and to have essential data to support road maintenance. Traditional texture assessment techniques often require expensive equipment and complex operations. To ensure cost sustainability and reduce traffic closure times, this study proposes a rapid, cost-effective, and non-invasive surface texture assessment technique. This technology consists of capturing a set of images of a road surface with a mobile phone; then, the images are used to reconstruct the 3D surface with photogrammetric processing and derive the roughness parameters to assess the pavement texture. The results indicate that pavement images taken by a mobile phone can reconstruct the 3D surface and extract texture features with accuracy, meeting the requirements of a time-effective documentation. To validate the effectiveness of this technique, the surface structure of the pavement was analyzed in situ using a 3D structured light projection scanner and rigorous photogrammetry with a high-end reflex camera. The results demonstrated that increasing the point cloud density can enhance the detail level of the real surface 3D representation, but it leads to variations in road surface roughness parameters. Therefore, appropriate density should be chosen when performing three-dimensional reconstruction using mobile phone images. Mobile phone photogrammetry technology performs well in detecting shallow road surface textures but has certain limitations in capturing deeper textures. The texture parameters and the Abbott curve obtained using all three methods are comparable and fall within the same range of acceptability. This finding demonstrates the feasibility of using a mobile phone for pavement texture assessments with appropriate settings.

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Publication Details

Journal
Sustainability
Published
2024-11-05
DOI
https://doi.org/10.3390/su16229630
Citations
4
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
1.04
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article

Low-Cost and Contactless Survey Technique for Rapid Pavement Texture Assessment Using Mobile Phone Imagery

Valeria Vignali, Claudio Lantieri, Margherita Pazzini, Valentina Alena Girelli et al.
4 citations
Sustainability
Infrastructure Maintenance and Monitoring
1.04
article

Low-Cost and Contactless Survey Technique for Rapid Pavement Texture Assessment Using Mobile Phone Imagery

Valeria Vignali, Claudio Lantieri, Margherita Pazzini, Valentina Alena Girelli, Marco Bruno, Anna Forte, Zhenlong Gong
article en
4 citations

Abstract

Collecting pavement texture information is crucial to understand the characteristics of a road surface and to have essential data to support road maintenance. Traditional texture assessment techniques often require expensive equipment and complex operations. To ensure cost sustainability and reduce traffic closure times, this study proposes a rapid, cost-effective, and non-invasive surface texture assessment technique. This technology consists of capturing a set of images of a road surface with a mobile phone; then, the images are used to reconstruct the 3D surface with photogrammetric processing and derive the roughness parameters to assess the pavement texture. The results indicate that pavement images taken by a mobile phone can reconstruct the 3D surface and extract texture features with accuracy, meeting the requirements of a time-effective documentation. To validate the effectiveness of this technique, the surface structure of the pavement was analyzed in situ using a 3D structured light projection scanner and rigorous photogrammetry with a high-end reflex camera. The results demonstrated that increasing the point cloud density can enhance the detail level of the real surface 3D representation, but it leads to variations in road surface roughness parameters. Therefore, appropriate density should be chosen when performing three-dimensional reconstruction using mobile phone images. Mobile phone photogrammetry technology performs well in detecting shallow road surface textures but has certain limitations in capturing deeper textures. The texture parameters and the Abbott curve obtained using all three methods are comparable and fall within the same range of acceptability. This finding demonstrates the feasibility of using a mobile phone for pavement texture assessments with appropriate settings.

SustainabilityVol. 16(22)
University of Science and Technology Beijing (CN), University of Bologna (IT)
Openalex Percentile: Top 25%
Infrastructure Maintenance and Monitoring
1.04
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