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.
Version 1.0 Multi-Class Severity-Annotated Controller Area Network (CAN) Dataset for Intrusion Detection Dataset Availability and Description To facilitate reproducible research in automotive cybersecurity, the dataset developed in this study has been made publicly available thro…
Dataset Description Bitemporal Sentinel-1 & Sentinel-2 Dataset for Deforestation and Forestation Monitoring in Sumatra and Kalimantan Abstract & Overview This dataset provides a large-scale collection of 45,770 remote sensing image tiles specifically curated for bitemporal change…
Falling has been one of the major concerns and threats to the independence of the elderly in their daily lives. With the worldwide significant growth of the aging population, it is essential to have a promising solution of fall detection which is able to operate at high accuracy…
The Microgreen Master Dataset (Public Version) is an open RGB image dataset developed to support research in computer vision, artificial intelligence, and smart agriculture. The dataset is intended for training and evaluating machine learning models for automatic classification o…
This dataset release represents Part 4 of the comprehensive young trefoil crop agricultural analysis project. While Part 1, Part 2 and Part 3 provided the raw image captures and camera parameters and machine-learning-ready image tiles. Part 4 delivers fully processed, georeferenc…
This dataset release represents Part 4 of the comprehensive young trefoil crop agricultural analysis project. While the four previous parts provided the raw image captures and camera parameters and machine-learning-ready image tiles. Part 5 delivers fully processed, georeferenced…