Histogram‐Based Approach for Automated Pavement‐Crack Sensing

Publisher Website
Google Scholar
Abstract
This paper presents work towards the use of histogram‐based machine vision for the automated sensing of unprepared cracks in highway pavement. Machine vision is being developed for purposes of identifying and locating cracks in the size range from 1/8 to 1 in. (3–25 mm) in width in both asphalt concrete (AC) and portland cement concrete (PCC) pavements. The machine vision being developed is intended to work in real time in an automated crack‐sealing machine that will operate at approximately 2 mph. The method is based on segmenting the pavement image into a grid of tiles, and then representing each tile's properties by histogram‐based parameters. Comparison to a localized set of tiles provides information of crack existence and direction. This paper presents preliminary results showing the capability of the approach to find unprepared cracks in real pavements, and additionally, remaining work that will lead to its real‐time implementation is discussed.

This publication has 4 references indexed in Scilit: