RSCD: Road Surface Image 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)路面状态预览对于提升自动驾驶车辆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的道路与气象条件场景。原始图像由车载相机采集，随后仅裁剪出包含路面区域的图像块。上述图像被划分为27个类别，覆盖摩擦等级、路面材质与路面不平整性三类属性。该数据集分为训练集（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的性能。更多详情请访问：ztsrxh/RSCD-Road_Surface_Classification_Dataset: A large-scale road surface image classification dataset for driving assistance applications (github.com)