  Existing road pothole detection approaches can be classified as computer
vision-based or machine learning-based. The former approaches typically employ
2-D image analysis/understanding or 3-D point cloud modeling and segmentation
algorithms to detect road potholes from vision sensor data. The latter
approaches generally address road pothole detection using convolutional neural
networks (CNNs) in an end-to-end manner. However, road potholes are not
necessarily ubiquitous and it is challenging to prepare a large well-annotated
dataset for CNN training. In this regard, while computer vision-based methods
were the mainstream research trend in the past decade, machine learning-based
methods were merely discussed. Recently, we published the first stereo
vision-based road pothole detection dataset and a novel disparity
transformation algorithm, whereby the damaged and undamaged road areas can be
highly distinguished. However, there are no benchmarks currently available for
state-of-the-art (SoTA) CNNs trained using either disparity images or
transformed disparity images. Therefore, in this paper, we first discuss the
SoTA CNNs designed for semantic segmentation and evaluate their performance for
road pothole detection with extensive experiments. Additionally, inspired by
graph neural network (GNN), we propose a novel CNN layer, referred to as graph
attention layer (GAL), which can be easily deployed in any existing CNN to
optimize image feature representations for semantic segmentation. Our
experiments compare GAL-DeepLabv3+, our best-performing implementation, with
nine SoTA CNNs on three modalities of training data: RGB images, disparity
images, and transformed disparity images. The experimental results suggest that
our proposed GAL-DeepLabv3+ achieves the best overall pothole detection
accuracy on all training data modalities.
