Impact of image resolution on pavement distress detection using picucha methodology

rehabilitation design and management, allowing public managers to maximize the value of the
continuously limited budgets for road improvements and maintenance. Manual pavement distress
surveys are labor-intensive, expensive and unsafe for highly-trafficked highways. Over the years,
automated surveys using various hardware devices have been developed and improved for
pavement field data collection to solve the problems associated with manual surveys. However,
the reliable distress detection software and the data analysis remain challenging. This study
focused on the analysis of a newly-developed pavement distress classification algorithm, called the
PICture Unsupervised Classification with Human Analysis (PICUCHA) method, particularly the
impact of image resolutions on its classification accuracy. The results show that a non-linear
relationship exists between the classification accuracy and the image resolution, suggesting that
images with a resolution around 1.24 mm/pixel may provide the optimal classification accuracy
when using the PICUCHA method. The findings of this study can help to improve more effective
uses of the specialize software for pavement distress classification, to support decision makers to
choose cameras according to their budgets and desired survey accuracy, and to evaluate how
existing cameras will perform if used with PICUCHA.