Drone model identification by convolutional neural network from video stream
| Autor: | Ivan Petrunin, Mariusz Wisniewski, Zeeshan A. Rana |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Orientation (computer vision) business.industry System identification airport security convolutional neural network anti-uav synthetic drones Pattern recognition Solid modeling Convolutional neural network Drone synthetic images symbols.namesake drones Gaussian noise Benchmark (computing) symbols domain randomization Artificial intelligence business Unmanned Aerial Vehicles |
| Popis: | We present a convolutional neural network model that correctly identifies drone models in real-life video streams of flying drones. To achieve this, we show a method of generating synthetic drone images. To create a diverse dataset, the simulation parameters (such as drone textures, lighting, and orientation) are randomized. This synthetic dataset is used to train a convolutional neural network to identify the drone model: DJI Phantom, DJI Mavic, or DJI Inspire. The model is then tested on a real-life Anti-UAV dataset of flying drones. The benchmark results show that the DenseNet201 architecture performed the best. Adding Gaussian noise to the training dataset and performing full training (as opposed to freezing layers) shows the best results. The model shows an average accuracy of 92.4%, and an average precision of 88.6% on the test dataset. |
| Databáze: | OpenAIRE |
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