Unmanned Aerial Vehicles Dataset
- 1. KIOS Research and Innovation Center of Excellence, University of Cyprus
Contributors
Data collector:
- 1. KIOS Research and Innovation Center of Excellence, University of Cyprus
Description
Unmanned Aerial Vehicles Dataset:
The Unmanned Aerial Vehicle (UAV) Image Dataset consists of a collection of images containing UAVs, along with object annotations for the UAVs found in each image. The annotations have been converted into the COCO, YOLO, and VOC formats for ease of use with various object detection frameworks. The images in the dataset were captured from a variety of angles and under different lighting conditions, making it a useful resource for training and evaluating object detection algorithms for UAVs. The dataset is intended for use in research and development of UAV-related applications, such as autonomous flight, collision avoidance and rogue drone tracking and following. The dataset consists of the following images and detection objects (Drone):
| Subset | Images | Drone |
| Training | 768 | 818 |
| Validation | 384 | 402 |
| Testing | 383 | 400 |
It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).
**NOTE** If you use this dataset in your research/publication please cite us using the following
Rafael Makrigiorgis, Nicolas Souli, & Panayiotis Kolios. (2022). Unmanned Aerial Vehicles Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7477569
Files
Annotations.zip
Additional details
References
- N. Souli, R. Makrigiorgis, P. Kolios and G. Ellinas, "Cooperative Relative Positioning using Signals of Opportunity and Inertial and Visual Modalities," 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 2021, pp. 1-7, doi: 10.1109/VTC2021-Spring51267.2021.9449064.
- N. Souli et al., "HorizonBlock: Implementation of an Autonomous Counter-Drone System," 2020 International Conference on Unmanned Aircraft Systems (ICUAS), 2020, pp. 398-404, doi: 10.1109/ICUAS48674.2020.9213871.