RSCD: Road Surface Classification Dataset with Detailed Annotations for Driving Assistance

The preview of the road surface states is essential for improving the safety and the ride comfort of autonomous vehicles. This dataset consists of 1 million (240 x 360 pixels) road surface images captured under a wide range of road and weather conditions in China. The original pictures are acquired with a vehicle-mounted camera and then the patches containing only the road surface area are cropped. The images are classified into 27 categories, containing both the friction level, material, and unevenness properties. The dataset is divided into train-set(~960k samples), validation-set(~20k samples), test-set(~50k samples) . This large-scale dataset is useful for developing vision-based road sensing modules to improve the performance of the driving assistance systems. More details ,please visit Github: ztsrxh/RSCD-Road_Surface_Classification_Dataset: A large-scale road surface image classification dataset for driving assistance applications (github.com)

路面状态的前置感知对于提升自动驾驶车辆（Autonomous Vehicles）的行驶安全性与乘坐舒适性至关重要。本数据集包含100万张分辨率为240×360像素的路面图像，采集自中国境内多样化的道路与气象环境。原始图像由车载摄像头（Vehicle-mounted Camera）采集，随后裁剪出仅包含路面区域的图像块。该数据集共涵盖27个分类类别，覆盖路面摩擦等级、材质与平整度三大核心属性。数据集划分为训练集（约96万样本）、验证集（约2万样本）与测试集（约5万样本）。此大规模数据集可用于开发基于视觉的道路感知模块，以提升驾驶辅助系统（Driving Assistance Systems）的整体性能。更多详情请访问GitHub仓库：ztsrxh/RSCD-Road_Surface_Classification_Dataset——一款面向驾驶辅助应用的大规模路面图像分类数据集（github.com）

搜集汇总

背景与挑战

背景概述

RSCD数据集是一个包含100万张道路表面图像的大规模分类数据集，图像覆盖多种道路和天气条件，分为27个类别，适用于自动驾驶辅助系统的开发。数据集包含训练集、验证集和测试集，支持基于视觉的道路感知模块的研究。