Assistive Belt Outdoor Navigation Dataset for Visually Impaired: 15-Class Object Detection Dataset (YOLO Format)

This dataset was developed to support an assistive belt system designed to help visually impaired individuals navigate outdoor environments safely. It contains annotated images covering 15 object classes relevant to outdoor navigation: person, car, bus, truck, bicycle, motorcycle, tree, trash (bin), pedestrian crossing signal (green light), pedestrian crossing signal (red light), crosswalk, door, stairs, stop sign, and bench. The dataset combines images from open-source datasets with manually collected and annotated images. All annotations are provided in YOLO format (bounding boxes with class labels), making the dataset ready for training object detection models (e.g., YOLOv5/v8) for real-time outdoor obstacle and landmark detection in assistive navigation applications. Intended use cases include training and benchmarking computer vision models for wearable/embedded assistive technology aimed at improving outdoor mobility and safety for visually impaired users. This dataset incorporates images and annotations derived from the COCO dataset (CC BY 4.0) and the Open Images dataset (CC BY 4.0), in addition to manually collected and annotated images.