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Drone Model Classification Using Convolutional Neural Network Trained on Synthetic Data
| Content Provider | MDPI |
|---|---|
| Author | Wisniewski, Mariusz Rana, Zeeshan A. Petrunin, Ivan |
| Copyright Year | 2022 |
| Description | 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. |
| Starting Page | 218 |
| e-ISSN | 2313433X |
| DOI | 10.3390/jimaging8080218 |
| Journal | Journal of Imaging |
| Issue Number | 8 |
| Volume Number | 8 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-12 |
| Access Restriction | Open |
| Subject Keyword | Journal of Imaging Unmanned Aerial Vehicles Drones Airport Security Convolutional Neural Network Synthetic Images Synthetic Data Domain Randomization Drone Detection Drone Classification Drone Identification Artificial Intelligence |
| Content Type | Text |
| Resource Type | Article |
