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Proceedings Paper

Pavement distress detection and severity analysis
Author(s): E. Salari; G. Bao
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Paper Abstract

Automatic recognition of road distresses has been an important research area since it reduces economic loses before cracks and potholes become too severe. Existing systems for automated pavement defect detection commonly require special devices such as lights, lasers, etc, which dramatically increase the cost and limit the system to certain applications. Therefore, in this paper, a low cost automatic pavement distress evaluation approach is proposed. This method can provide real-time pavement distress detection as well as evaluation results based on the color images captured from a camera installed on a survey vehicle. The entire process consists of two main parts: pavement surface extraction followed by pavement distress detection and classification. In the first part, a novel color segmentation method based on a feed forward neural network is applied to separate the road surface from the background. In the second part, a thresholding technique based on probabilistic relaxation is utilized to separate distresses from the road surface. Then, by inputting the geometrical parameters obtained from the detected distresses into a neural network based pavement distress classifier, the defects can be classified into different types. Simulation results are given to show that the proposed method is both effective and reliable on a variety of pavement images.

Paper Details

Date Published: 7 February 2011
PDF: 10 pages
Proc. SPIE 7877, Image Processing: Machine Vision Applications IV, 78770C (7 February 2011); doi: 10.1117/12.876724
Show Author Affiliations
E. Salari, The Univ. of Toledo (United States)
G. Bao, The Univ. of Toledo (United States)

Published in SPIE Proceedings Vol. 7877:
Image Processing: Machine Vision Applications IV
David Fofi; Philip R. Bingham, Editor(s)

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