doi:10.3850/978-981-08-7920-4_S3-I007-cd

ABSTRACT

Pavement condition assessment is essential when developing road network maintenance programs. In practice, the data collection process is to a large extent automated.However, pavement distress detection (cracks, potholes, etc.) is mostly performedmanually, which is labor-intensive and time-consuming. Existing pothole detection methods either rely on complete 3D surface reconstruction, which comes along with high equipment and computation costs, or make use of acceleration data, which can only provide preliminary and rough condition surveys. In this paper we present a method for automated pothole detection in asphalt pavement images. An image is first segmented into defect and non-defect regions using histogram shape-based thresholding. Based on the geometric properties of a defect region the potential pothole shape is approximated utilizing morphological thinning and elliptic regression. Subsequently, the texture inside a potential defect region is extracted and compared with the texture of the surrounding non-defect pavement in order to determine if the region of interest represents an actual pothole. This methodology has been implemented in a MATLAB prototype, trained and tested on 120 pavement images. The results showthat thismethod can detect potholes in asphalt pavement images with reasonable accuracy.

Keywords: Pavement assessment, Pothole detection, Visual sensing, Image processing.

© 2011 Research Publishing Services. All rights reserved.