M2S-RoAD: Multi-Modal Semantic Segmentation for Road Damage Using Camera and LiDAR Data

• URL: http://arxiv.org/abs/2504.10123v1
• Date: Mon, 14 Apr 2025 11:32:01 GMT
• Title: M2S-RoAD: Multi-Modal Semantic Segmentation for Road Damage Using Camera and LiDAR Data
• Authors: Tzu-Yun Tseng, Hongyu Lyu, Josephine Li, Julie Stephany Berrio, Mao Shan, Stewart Worrall,
• Abstract summary: This paper introduces M2S-RoAD, a dataset for the semantic segmentation of different classes of road damage.<n>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.
• Score: 9.967263440745432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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