Title: City-Scale Road Audit System using Deep Learning,
Abstract: Road networks in cities are massive and is a critical component of mobility.
Fast response to defects, that can occur not only due to regular wear and tear
but also because of extreme events like storms, is essential. Hence there is a
need for an automated system that is quick, scalable and cost-effective for
gathering information about defects. We propose a system for city-scale road
audit, using some of the most recent developments in deep learning and semantic
segmentation. For building and benchmarking the system, we curated a dataset
which has annotations required for road defects. However, many of the labels
required for road audit have high ambiguity which we overcome by proposing a
label hierarchy. We also propose a multi-step deep learning model that segments
the road, subdivide the road further into defects, tags the frame for each
defect and finally localizes the defects on a map gathered using GPS. We
analyze and evaluate the models on image tagging as well as segmentation at
different levels of the label hierarchy.