Synthetic Interior Damage Dataset (SIDD)

The Synthetic Interior Damage Dataset (SIDD) dataset consists of 663 annotated images captured inside the cabin of a Citroën Ami using a single fisheye camera mounted on the vehicle ceiling, providing a 180° field of view that covers the entire interior. Synthetic damages were digitally added to the seat surfaces, including the seat base and backrest areas, simulating realistic wear, scratches, and tears. The dataset is designed to support research in computer vision, anomaly detection, and damage localization tasks within automotive interiors. Each image is annotated in YOLO detection format and prepared for use in supervised machine learning experiments. The dataset contains 478 labeled instances, and all annotations correspond to a single class: 0 – damage. The dataset also includes a helper script, split_yolo_dataset.py, which can be used to divide the dataset into training, validation, and test sets. The script: Copies all images to the corresponding split folders. Copies existing YOLO-format label files to the same splits. Creates empty label files for images without annotations, allowing them to serve as negative examples. Ensures the split proportions match the configured ratios (default: 70% train, 20% validation, 10% test). This script facilitates dataset organization for training YOLO object detection models and ensures that images without synthetic damages are properly handled.合成内饰损伤数据集（Synthetic Interior Damage Dataset, SIDD）包含663张带标注的图像，这些图像采集自雪铁龙AMI（Citroën Ami）的座舱内部，由安装在车辆顶棚的单台鱼眼相机拍摄，具备覆盖整个座舱的180°视场角。研究人员通过数字方式在座椅表面（包括座椅坐垫与靠背区域）添加了合成损伤，以模拟真实的磨损、刮擦与撕裂痕迹。 本数据集旨在支撑汽车内饰场景下的计算机视觉、异常检测以及损伤定位相关研究。所有图像均采用YOLO检测格式进行标注，可直接用于监督式机器学习实验。本数据集共包含478个标注实例，所有标注仅对应单一类别：0——损伤。 本数据集还附带了辅助脚本split_yolo_dataset.py，可用于将数据集划分为训练集、验证集与测试集。该脚本功能如下： - 将所有图像复制至对应划分文件夹中； - 将现有的YOLO格式标签文件复制至对应划分目录； - 为无标注的图像生成空标签文件，使其可作为负样本使用； - 确保数据集划分比例符合预设配置（默认比例为：训练集70%、验证集20%、测试集10%）。 该脚本可简化用于YOLO目标检测模型训练的数据集组织流程，并确保无合成损伤的图像得到妥善处理。