  Potholes are one of the most common forms of road damage, which can severely
affect driving comfort, road safety and vehicle condition. Pothole detection is
typically performed by either structural engineers or certified inspectors.
This task is, however, not only hazardous for the personnel but also extremely
time-consuming. This paper presents an efficient pothole detection algorithm
based on road disparity map estimation and segmentation. We first generalize
the perspective transformation by incorporating the stereo rig roll angle. The
road disparities are then estimated using semi-global matching. A disparity map
transformation algorithm is then performed to better distinguish the damaged
road areas. Finally, we utilize simple linear iterative clustering to group the
transformed disparities into a collection of superpixels. The potholes are then
detected by finding the superpixels, whose values are lower than an adaptively
determined threshold. The proposed algorithm is implemented on an NVIDIA RTX
2080 Ti GPU in CUDA. The experiments demonstrate the accuracy and efficiency of
our proposed road pothole detection algorithm, where an accuracy of 99.6% and
an F-score of 89.4% are achieved.
