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