用相机与激光雷达数据实现道路损伤语义分割，助力自动驾驶安全
M2S-RoAD: Multi-Modal Semantic Segmentation for Road Damage Using Camera and LiDAR Data
- 融合相机与激光雷达数据进行多模态语义分割
- 构建包含9类损伤的澳大利亚乡村道路数据集
- 填补农村道路损伤检测研究空白，适合自动驾驶应用
道路损伤会给人类驾驶员和自动驾驶车辆带来安全与舒适挑战，尤其在因巡检和维护频率较低而问题更突出的农村地区。自动化检测路面退化可为自动驾驶系统和驾驶辅助系统提供输入，提升道路安全。当前该领域研究主要聚焦于城市环境，受公共数据集驱动，而农村地区关注度显著不足。本文提出M2S-RoAD，一个用于道路损伤语义分割的数据集，数据采集自澳大利亚新南威尔士州多个城镇，标注了九类不同类型的道路损伤。该数据集将在论文被接收后公开。
原文摘要 · Abstract (English)
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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