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A platform for research: civil engineering, architecture and urbanism
Automated Pothole Distress Assessment Using Asphalt Pavement Video Data
Potholes, as a severe type of pavement distress, are currently identified and assessed manually in pavement-maintenance programs. This manual process is time-consuming and labor-intensive. Existing methods for automated pothole detection either rely on expensive and high-maintenance range sensors or make use of acceleration data, which only apply when the pothole is on the tires’ path. The authors’ previous work has proposed and validated a camera-based pothole-detection method. However, this method is limited to single frames and cannot determine the severity of potholes. This paper presents a novel method that addresses these issues by incrementally updating a representative texture template for intact pavement regions and using a vision tracker to reduce the computational effort, improve the detection reliability, and count potholes efficiently. The improved method was implemented and tested on real data. The results indicate a significant capability and performance increase of this method over its predecessor.
Automated Pothole Distress Assessment Using Asphalt Pavement Video Data
Potholes, as a severe type of pavement distress, are currently identified and assessed manually in pavement-maintenance programs. This manual process is time-consuming and labor-intensive. Existing methods for automated pothole detection either rely on expensive and high-maintenance range sensors or make use of acceleration data, which only apply when the pothole is on the tires’ path. The authors’ previous work has proposed and validated a camera-based pothole-detection method. However, this method is limited to single frames and cannot determine the severity of potholes. This paper presents a novel method that addresses these issues by incrementally updating a representative texture template for intact pavement regions and using a vision tracker to reduce the computational effort, improve the detection reliability, and count potholes efficiently. The improved method was implemented and tested on real data. The results indicate a significant capability and performance increase of this method over its predecessor.
Automated Pothole Distress Assessment Using Asphalt Pavement Video Data
Koch, Christian (author) / Jog, Gauri M. (author) / Brilakis, Ioannis (author)
Journal of Computing in Civil Engineering ; 27 ; 370-378
2012-05-18
92013-01-01 pages
Article (Journal)
Electronic Resource
English
Automated Pothole Distress Assessment Using Asphalt Pavement Video Data
| Online Contents | 2013
Automated Pothole Distress Assessment Using Asphalt Pavement Video Data
| British Library Online Contents | 2013