标题： RoadFormer：自动驾驶道路表面分类中的局部-全局特征融合 显示英文标题

标题： RoadFormer : Local-Global Feature Fusion for Road Surface Classification in Autonomous Driving

摘要： 道路表面分类（RSC）的目的是利用路面特征来识别路面的粗糙度、干湿状态以及材料信息。 由于其能够有效提升道路安全和交通管理的能力，在近年来受到了广泛关注。 在自动驾驶中，准确的道路表面分类允许车辆更好地理解道路环境，调整驾驶策略，从而确保更安全高效的驾驶体验。 长期以来，基于视觉的道路表面分类一直受到青睐。 然而，现有的视觉分类方法忽视了对路面类型细粒度分类（如相似的路面纹理）的探索。 在这项工作中，我们提出了一种纯视觉的细粒度RSC方法，用于自动驾驶场景，通过卷积模块和Transformer模块的堆叠融合局部和全局特征信息。 我们进一步探索了局部和全局特征提取模块的堆叠策略，以找到最优的特征提取策略。 此外，由于细粒度任务也面临着类内差异较大而类间差异较小的挑战，我们提出了一个前景-背景模块（FBM），能够有效地提取路面的细粒度上下文特征，增强复杂路面的分类能力。 在包含一百万个样本的大规模路面数据集以及从该数据集中重新组织的简化数据集上进行的实验分别达到了92.52%和96.50%的Top-1分类准确率，相比最先进的方法提高了5.69%到12.84%。 这些结果表明，RoadFormer在RSC任务中优于现有方法，在提高自动驾驶系统中路面感知可靠性方面取得了显著进展。 显示英文摘要

摘要： The classification of the type of road surface (RSC) aims to utilize pavement features to identify the roughness, wet and dry conditions, and material information of the road surface. Due to its ability to effectively enhance road safety and traffic management, it has received widespread attention in recent years. In autonomous driving, accurate RSC allows vehicles to better understand the road environment, adjust driving strategies, and ensure a safer and more efficient driving experience. For a long time, vision-based RSC has been favored. However, existing visual classification methods have overlooked the exploration of fine-grained classification of pavement types (such as similar pavement textures). In this work, we propose a pure vision-based fine-grained RSC method for autonomous driving scenarios, which fuses local and global feature information through the stacking of convolutional and transformer modules. We further explore the stacking strategies of local and global feature extraction modules to find the optimal feature extraction strategy. In addition, since fine-grained tasks also face the challenge of relatively large intra-class differences and relatively small inter-class differences, we propose a Foreground-Background Module (FBM) that effectively extracts fine-grained context features of the pavement, enhancing the classification ability for complex pavements. Experiments conducted on a large-scale pavement dataset containing one million samples and a simplified dataset reorganized from this dataset achieved Top-1 classification accuracies of 92.52% and 96.50%, respectively, improving by 5.69% to 12.84% compared to SOTA methods. These results demonstrate that RoadFormer outperforms existing methods in RSC tasks, providing significant progress in improving the reliability of pavement perception in autonomous driving systems.