  Conventional approaches for addressing road safety rely on manual
interventions or immobile CCTV infrastructure. Such methods are expensive in
enforcing compliance to traffic rules and do not scale to large road networks.
This paper proposes a simple mobile imaging setup to address several common
problems in road safety at scale. We use recent computer vision techniques to
identify possible irregularities on roads, the absence of street lights, and
defective traffic signs using videos from a moving camera-mounted vehicle.
Beyond the inspection of static road infrastructure, we also demonstrate the
mobile imaging solution's applicability to spot traffic violations. Before
deploying our system in the real-world, we investigate the strengths and
shortcomings of computer vision techniques on thirteen condition-based
hierarchical labels. These conditions include different timings, road type,
traffic density, and state of road damage. Our demonstrations are then carried
out on 2000 km of unconstrained road scenes, captured across an entire city.
Through this, we quantitatively measure the overall safety of roads in the city
through carefully constructed metrics. We also show an interactive dashboard
for visually inspecting and initiating action in a time, labor and
cost-efficient manner. Code, models, and datasets used in this work will be
publicly released.
