상세정보
학술대회
Flying Drone Classification based on Visualization of Acoustic Signals with Deep Neural Networks
- 발행일
- 202010
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.546-548
- 협약과제
- 20NR1100, 소음 및 영상신호 결합기반 무인기 검출 기술 개발, 김영일
- 초록
- 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.
- KSP 제안 키워드
- 3-channel, Acoustic signal, As loss, Classification Performance, Deep neural network(DNN), Performance benchmarks, Transfer learning