Drone Identification Method Based on Mixed Domain Attention Mechanism

被引：0
|
作者：
Xue S. [1 ,2 ]
Wei L. [1 ]
Gu C. [3 ]
Lu Q. [1 ]
机构：
[1] School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun
[2] Chongqing Research Institute, Changchun University of Science and Technology, Chongqing
[3] School of Information and Communications Engineering, Xi'an Jiaotong University
来源：
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2022年 / 56卷 / 10期
关键词：
drone; log Mel-spectrogram; mixed domain attention mechanism; neural network; voice recognition;
D O I：
10.7652/xjtuxb202210014
中图分类号：
学科分类号：
摘要：
An economical, convenient and undisturbed drone detection method using sound and multiscale group convolution network with attention mechanism in mixed domain of channel space (ECSANet) is proposed in the context of susceptibility to electronic interference in identification of drones by radar and radio, and the interference of light and obstruction in identification of drones by images in public environments such as urban parks, squares and large amusement parks. Firstly, nine kinds of sound dataset of civil drones are established, and their logarithmic Mel spectra and dynamic characteristics are extracted. Secondly, based on packet convolution, channel shuffling and residual structure, a multi-scale group convolution network with channel shuffle (MSSGNet) is designed to reduce the network parameters and avoid over fitting. Then, the efficient channel and spatial attention (ECSA) is designed to extract more and more effective features of drone sounds. Finally, the ECSA is inserted into the MSSGNet to form an improved multiscale group convolution network with attention mechanism in mixed domain of channel space (ECSANet), offering a new method for sound recognition of drones. The designed ECSANet is used to identify the self-built civil drone sound dataset and environmental sound dataset urbansound8k. The results reveal that when compared with benchmark networks such as ResNetl 8, ResNet34, ResNeXt18, and MobileNetV2, the MSSGNet has fewer network parameters but a higher identification accuracy (up to 95. 1%). The ECSA can be inserted into a variety of networks to improve identification accuracy of network models without introducing too many parameters, and it works well for sound classification tasks like drones. As compared with the MSSGNet, the improved ECSANet has an identification accuracy of 95. 9 %, an increase of 0. 8 percent, demonstrating the superiority and feasibility in identifying a small sample of drones. © 2022 Xi'an Jiaotong University. All rights reserved.
引用
收藏
页码：141 / 150
页数：9
相关论文
共 26 条
  • [1] LUO Junhai, WANG Zhiyan, A review of development and application of UAV detection and counter technology [J], Control and Decision, 37, 3, pp. 530-511, (2022)
  • [2] CHEN Weishi, HUANG Yifcng, LU Xianfeng, Survey on application of multi-sensor fusion in UAV detection technology, Modern Radar, 12, 6, pp. 15-29, (2020)
  • [3] AL-EMADI S, AL-ALI A, AL-ALI A., Audio-based drone detection and identification using deep learning techniques with dataset enhancement through generative adversarial networks, Sensors, 21, 15, (2021)
  • [4] ISMAIL M A A, BIERIG A., Identifying drone-related security risks by a laser vibrometer-based payload identification system, Proceedings of Laser Radar Technology and Applications, (2018)
  • [5] SEO Y, JANG B, IM S., Drone detection using convolutional neural networks with acoustic STFT features, 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 1-6, (2018)
  • [6] CASABLANCA P, ZHANG Yu, Acoustic-based UAV detection using late fusion of deep neural networks, Drones, 5, 3, (2021)
  • [7] KRIZHEVSKY A, SUTSKEVER I, HINTON G., Image net classification with deep convolutional neural networks [J], Communications of the ACM, 60, 6, pp. 81-90, (2017)
  • [8] LI Yunhong, LIANG Sicheng, JIA Kaili, Et al., An improved speech recognition method based on DNN-HMM model [J], Applied Acoustics, 38, 3, pp. 371-377, (2019)
  • [9] DUMITRESCU C, MINEA M, COSTEA I M, Et al., Development of an acoustic system for UAV detection [J], Sensors, 20, 17, (2020)
  • [10] JIN Shuaipu, WANG Xiufeng, DU Leilci, Et al., Evaluation and modeling of automotive transmission whine noise quality based on MFCC and CNN, Applied Acoustics, 172, (2021)
123