City-Scale Road Audit System using Deep Learning
This work addresses the need for quick, scalable, and cost-effective road maintenance for city mobility, though it appears incremental by applying existing deep learning techniques to a new dataset.
The authors tackled the problem of automating city-scale road defect detection by proposing a deep learning system that segments roads, identifies defects, and maps them using GPS, achieving evaluation on image tagging and segmentation tasks.
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.