IMPACT OF IMAGE RESOLUTION ON PAVEMENT DISTRESS DETECTION USING PICUCHA METHODOLOGY

Abstract

An accurate and regular survey of the road surface distresses is a key factor for pavementrehabilitation design and management, allowing public managers to maximize the value of thecontinuously limited budgets for road improvements and maintenance. Manual pavement distresssurveys are labor-intensive, expensive and unsafe for highly-trafficked highways. Over the years,automated surveys using various hardware devices have been developed and improved forpavement field data collection to solve the problems associated with manual surveys. However,the reliable distress detection software and the data analysis remain challenging. This studyfocused on the analysis of a newly-developed pavement distress classification algorithm, called thePICture Unsupervised Classification with Human Analysis (PICUCHA) method, particularly theimpact of image resolutions on its classification accuracy. The results show that a non-linearrelationship exists between the classification accuracy and the image resolution, suggesting thatimages with a resolution around 1.24 mm/pixel may provide the optimal classification accuracywhen using the PICUCHA method. The findings of this study can help to improve more effectiveuses of the specialize software for pavement distress classification, to support decision makers tochoose cameras according to their budgets and desired survey accuracy, and to evaluate howexisting cameras will perform if used with PICUCHA.