  With the advent of self-driving cars and autonomous robots, it is imperative
to detect road impairments like cracks and potholes and to perform necessary
evading maneuvers to ensure fluid journey for on-board passengers or equipment.
We propose a fully autonomous robust real-time road crack and pothole detection
algorithm which can be deployed on any GPU based conventional processing boards
with an associated camera. The approach is based on a deep neural net
architecture which detects cracks and potholes using texture and spatial
features. We also propose pre-processing methods which ensure real-time
performance. The novelty of the approach lies in using texture- based features
to differentiate between crack surfaces and sound roads. The approach performs
well in large viewpoint changes, background noise, shadows, and occlusion. The
efficacy of the system is shown on standard road crack datasets.
