CAVI-14: A Real-Time Vehicle Object Image Dataset for Autonomous Driving

CAVI-14 (Camera-based Vehicle Image dataset with 14 categories) is a camera-based image dataset designed to support research in real-time object detection for intelligent transportation systems and autonomous driving. The dataset consists of 2,661 high-resolution images, collected using a Samsung Galaxy A15 smartphone camera around a university campus area in South Korea, capturing diverse urban traffic environments. The dataset includes 9,082 annotated objects across 14 vehicle-related object classes, specifically: • Vehicles & Transport: car, bus, pickup, lorry, ambulance, motorcycle, bicycle, e-bike • Pedestrian & Road Elements: pedestrian, zebra crossing, sidewalk, speed bump, traffic signal, road divider Each image is annotated with precise bounding boxes. The annotations are provided in two widely-used formats: • YOLO format (.txt) for real-time detection models • Pascal VOC format (.xml) for traditional detection pipelines The dataset is organized into three main directories: • train folder contains training images and their corresponding annotation files • test folder contains testing images and annotation files • annotations folder includes metadata in .xml format (object name, coordinates, image size, etc.) This structured and standardized format allows researchers to directly use the dataset for training and validating object detection models. A baseline evaluation using the YOLOv5s model achieved promising performance, confirming the dataset's suitability for benchmarking deep learning-based vehicle detection algorithms. CAVI-14 contributes a diverse and realistic set of labeled street-level traffic images, helping advance computer vision research in traffic monitoring, smart cities, and autonomous navigation.CAVI-14（14类别摄像头车辆图像数据集，Camera-based Vehicle Image dataset with 14 categories）是一款基于摄像头的图像数据集，旨在支撑智能交通系统与自动驾驶领域的实时目标检测研究。该数据集包含2661张高分辨率图像，由三星Galaxy A15智能手机摄像头在韩国某大学校园周边区域采集，涵盖多样化的城市交通场景。 该数据集共标注了9082个目标，涵盖14个与车辆相关的目标类别，具体如下： • 车辆与交通类：轿车、巴士、皮卡、货车、救护车、摩托车、自行车、电动自行车 • 行人与道路设施类：行人、斑马线、人行道、减速带、交通信号灯、道路分隔带 每张图像均配有精准的边界框标注。标注文件提供两种通用格式： • YOLO格式（.txt）：适用于实时检测模型 • Pascal VOC格式（.xml）：适用于传统检测流程 该数据集分为三个主要目录： • train文件夹：包含训练图像及其对应标注文件 • test文件夹：包含测试图像及其标注文件 • annotations文件夹：包含.xml格式的元数据（目标名称、坐标、图像尺寸等） 这种结构化、标准化的格式可让研究人员直接将该数据集用于目标检测模型的训练与验证。使用YOLOv5s模型开展的基线评估取得了优异性能，证实了该数据集适用于基于深度学习的车辆检测算法的性能基准测试。 CAVI-14提供了多样化且贴近真实场景的标注街面交通图像集，有助于推动交通监控、智慧城市与自主导航领域的计算机视觉研究。