Flying Drone Classification based on Visualization of Acoustic Signals with Deep Neural Networks
| Autor: | Soonyong Song, Young-Jin Kim, Young-Sung Son |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Short-time Fourier transform 020206 networking & telecommunications Pattern recognition 02 engineering and technology Signal Drone Visualization 0202 electrical engineering electronic engineering information engineering Spectrogram Deep neural networks 020201 artificial intelligence & image processing Artificial intelligence Transfer of learning business F1 score |
| Zdroj: | ICTC |
| Popis: | In this paper, we proposed acoustic signal visualization for flying drone classifications and provided performance benchmarks for backbone deep neural networks. To visualize acoustic signals, we transformed the signals to 3-channel images by spectrogram. We put the images into deep neural networks to train their weights by transfer learning. We evaluated our classifiers by accuracy, recall, precision, and F1 score as well as loss. In our experiments, we accomplished maximum 99% drone classification performance in terms of accuracy with our dataset. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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