Crack-pot: Autonomous Road Crack and Pothole Detection
This addresses road safety for autonomous systems, but it appears incremental as it builds on existing deep learning methods for a specific domain.
The paper tackles the problem of detecting road cracks and potholes for autonomous vehicles by proposing a deep neural network algorithm that uses texture and spatial features, achieving robust performance in various conditions as demonstrated on standard datasets.
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.