Vision for Road Inspection

Abstract

Road surface inspection in cities is for the most part, a task performed manually. Being a subjective and labor intensive process, it is an ideal candidate for automation. We propose a solution based on computer vision and data- driven methods to detect distress on the road surface. Our method works on images collected from a camera mounted on the windshield of a vehicle. We use an automatic pro- cedure to select images suitable for inspection based on lighting and weather conditions. From the selected data we segment the ground plane and use texture, color and loca- tion information to detect the presence of pavement distress. We describe an over-segmentation algorithm that identifies coherent image regions not just in terms of color, but also texture. We also discuss the problem of learning from unre- liable human-annotations and propose using a weakly su- pervised learning algorithm (Multiple Instance Learning) to train a classifier. We present results from experiments comparing the performance of this approach against multi- ple individual human labelers, with the ground-truth labels obtained from an ensemble of other human labelers. Fi- nally, we show results of pavement distress scores computed using our method over a subset of a citywide road network.