This article presents a novel road damage detection algorithm based on unsupervised disparity map segmentation. Firstly, a disparity map is transformed by minimizing an energy function with respect to stereo rig roll angle and road disparity projection model. Instead of solving this energy minimization problem using non-linear optimization techniques, we directly find its numerical solution. The transformed disparity map is then segmented using Otus’s thresholding method, and the damaged road areas can be extracted. The proposed algorithm requires no parameters when detecting road damage. The experimental results illustrate that our proposed algorithm performs both accurately and efficiently. The pixel-level road damage detection accuracy is approximately 97.56%. The source code is publicly available at: https://github.com/ruirangerfan/unsupervised_disparity_map_segmentation.git.
Road Damage Detection Based on Unsupervised Disparity Map Segmentation
IEEE Transactions on Intelligent Transportation Systems ; 21 , 11 ; 4906-4911
2020-11-01
9127016 byte
Article (Journal)
Electronic Resource
English
Unsupervised Segmentation of Road Images
British Library Conference Proceedings | 1997
|Road Surface Segmentation based on Vertically Local Disparity Histogram for Stereo Camera
Springer Verlag | 2017
|Road Segmentation Supervised by an Extended V-Disparity Algorithm for Autonomous Navigation
British Library Conference Proceedings | 2007
|