license:cc-by-4.0 size_categories:1K<n<10K format:imagefolder modality:image library:datasets library:mlcroissant region:us
---
license: cc-by-4.0
---

Part of the "Drone Model Classification Using Convolutional Neural Network Trained on Synthetic Data" paper (https://www.mdpi.com/2313-433X/8/8/218). 
Contains four classes:
- DJI Mavic
- DJI Phantom
- DJI Inspire
- No Drone

If you find this useful, please consider citing: 
```
@Article{jimaging8080218,
AUTHOR = {Wisniewski, Mariusz and Rana, Zeeshan A. and Petrunin, Ivan},
TITLE = {Drone Model Classification Using Convolutional Neural Network Trained on Synthetic Data},
JOURNAL = {Journal of Imaging},
VOLUME = {8},
YEAR = {2022},
NUMBER = {8},
ARTICLE-NUMBER = {218},
URL = {https://www.mdpi.com/2313-433X/8/8/218},
PubMedID = {36005461},
ISSN = {2313-433X},
ABSTRACT = {We present a convolutional neural network (CNN) that identifies drone models in real-life videos. The neural network is trained on synthetic images and tested on a real-life dataset of drone videos. To create the training and validation datasets, we show a method of generating synthetic drone images. Domain randomization is used to vary the simulation parameters such as model textures, background images, and orientation. Three common drone models are classified: DJI Phantom, DJI Mavic, and DJI Inspire. To test the performance of the neural network model, Anti-UAV, a real-life dataset of flying drones is used. The proposed method reduces the time-cost associated with manually labelling drones, and we prove that it is transferable to real-life videos. The CNN achieves an overall accuracy of 92.4%, a precision of 88.8%, a recall of 88.6%, and an f1 score of 88.7% when tested on the real-life dataset.},
DOI = {10.3390/jimaging8080218}
}
```