M2S-RoAD: Multi-Modal Semantic Segmentation for Road Damage Using Camera and LiDAR Data
This addresses the problem of road safety for human drivers and autonomous vehicles in rural areas, but it is incremental as it primarily provides a new dataset rather than a novel method.
The paper tackles the problem of automated road damage detection, particularly in rural areas, by introducing M2S-RoAD, a dataset for semantic segmentation of nine distinct types of road damage collected in New South Wales, Australia.
Road damage can create safety and comfort challenges for both human drivers and autonomous vehicles (AVs). This damage is particularly prevalent in rural areas due to less frequent surveying and maintenance of roads. Automated detection of pavement deterioration can be used as an input to AVs and driver assistance systems to improve road safety. Current research in this field has predominantly focused on urban environments driven largely by public datasets, while rural areas have received significantly less attention. This paper introduces M2S-RoAD, a dataset for the semantic segmentation of different classes of road damage. M2S-RoAD was collected in various towns across New South Wales, Australia, and labelled for semantic segmentation to identify nine distinct types of road damage. This dataset will be released upon the acceptance of the paper.