City-Scale Road Audit System using Deep Learning

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.

Authors

• Sudhir Yarram
• Girish Varma
• C. V. Jawahar

Keywords

• Roads
• Image segmentation
• Semantics
• Deep learning
• Global Positioning System
• Cameras
• Real-time systems
• Scalable
• Automatic System
• Deep Learning Models
• Road Network
• Semantic Segmentation
• Development Of Deep Learning
• Different Levels Of Hierarchy
• Image Tags
• Computer Vision
• Accelerometer
• Class Level
• Image Size
• Intersection Over Union
• Traffic Congestion
• Road Segments
• Spatial Pooling
• Feature Module
• Distribution Of Pixels
• Autonomous Navigation
• Pixel-level Annotations
• Fault Segments
• Intersection Over Union Score
• Semantic Segmentation Problem
• Crack Detection
• Semantic Segmentation Datasets
• Road Roughness