SaiVaibhavS/comprehensive-car-damage

该数据集用于训练和评估汽车损伤检测的机器学习模型，特别关注车辆前后部的损伤情况。包含高质量标注图像，分为六个类别：R_Normal（后部无损伤）、R_Crushed（后部挤压损伤）、R_Breakage（后部断裂损伤）、F_Normal（前部无损伤）、F_Crushed（前部挤压损伤）、F_Breakage（前部断裂损伤）。可用于构建自动车辆检测系统、保险理赔评估工具、道路安全分析等应用。数据集按类别标签分目录存储，六类数据均衡分布，并可能包含角度、光照条件等元数据。

This dataset is designed for training and evaluating machine learning models for car damage detection, specifically focusing on front and rear vehicle damages. It includes high-quality labeled images categorized into six distinct classes: R_Normal (Rear view of undamaged cars), R_Crushed (Rear view with crushed damage), R_Breakage (Rear view with visible breakage), F_Normal (Front view of undamaged cars), F_Crushed (Front view with crushed damage), F_Breakage (Front view with visible breakage). Can be used for building automated vehicle inspection systems, insurance claim assessment tools, road safety analytics, etc. Images are stored in directories named by class labels, with balanced distribution across six categories and may include metadata like angle, lighting conditions.