Classification of Moving Targets in Fields Based on Improved TCN Model

doi:10.19678/j.issn.1000-3428.0058750

Abstract

Abstract: Due to the complex background noises of the moving target signals in the fields, the classification methods of moving targets based on single-mode sound signals is limited by the low recognition rate and poor robustness.To address the problem, a network model is proposed based on multi-modal fusion of sound and vibration signals.The new model is constructed based on the Temporal Convolutional Network(TCN) model, which is modified by using the idea of dense connection in DenseNet.On this basis, the deep features of the four-channel sound signals and the single-channel vibration signals are extracted.Then the two kinds of signals are fused to obtain the final target classification result.At the same time, this paper uses the weighted loss function to solve the poor generalization performance caused by data imbalance.Experimental results show that the recognition accuracy of the proposed model reaches 92.92%, which is 6.63%~9.46% higher than that of the single-mode input network models, and the model has higher robustness.

Key words: sound and vibration signals, multi-modal fusion, Temporal Convolutional Network(TCN), dense connection, classification of moving targets

摘要： 野外运动目标信号的背景噪声复杂，利用单模态声音信号进行野外目标分类识别率低且鲁棒性差。针对该问题，提出一种基于声震多模态融合的网络模型。借鉴DenseNet网络密集连接的思想改进时域卷积网络，从而对四通道声音信号和单通道震动信号进行深层次的特征提取，并将两种信号相互融合得到最终的目标分类结果。同时，使用带权重的损失函数解决因数据不均衡导致的泛化性能差的问题。实验结果表明，融合网络的识别准确率达到92.92%，较单模态输入网络提高了6.63%~9.46%，且该网络具有较强的鲁棒性。

关键词: 声震信号, 多模态融合, 时域卷积网络, 密集连接, 运动目标分类

CLC Number:

TP18

FAN Yuying, LI Chengjuan, YI Qiang, LI Baoqing. Classification of Moving Targets in Fields Based on Improved TCN Model[J]. Computer Engineering, 2021, 47(9): 106-112.

范裕莹, 李成娟, 易强, 李宝清. 基于改进TCN模型的野外运动目标分类[J]. 计算机工程, 2021, 47(9): 106-112.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0058750

http://www.ecice06.com/EN/Y2021/V47/I9/106

Figures/Tables 9

References

[1] PADMAVATHI G, SHANMUGAPRIYA D, KALAIVANI M.Acoustic signal based feature extraction for vehicular classification[C]//Proceedings of the 3rd International Conference on Advanced Computer Theory and Engineering. Washington D.C., USA:IEEE Press, 2010:11-14.
[2] ZHOU Q, TONG G, XIE D, et al.A seismic-based feature extraction algorithm for robust ground target classification[J].IEEE Signal Processing Letters, 2012, 19(10):639-642.
[3] ALJAAFREH A, DONG L.An evaluation of feature extraction methods for vehicle classification based on acoustic signals[C]//Proceedings of 2010 International Conference on Networking, Sensing and Control.Washington D.C., USA:IEEE Press, 2010:570-575.
[4] NTALAMPIRAS S.Moving vehicle classification using wireless acoustic sensor networks[J].IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, 2(2):129-138.
[5] 刘彩霞, 方建军, 刘艳霞, 等.基于多类特征融合的极限学习在四足机器人野外地形识别中的应用[J].电子测量与仪器学报, 2018, 32(2):97-105. LIU C X, FANG J J, LIU Y X, et al.Application of extreme learning based on multi-class feature fusion in field terrain recognition of quadruped robots[J].Journal of Electronic Measurement and Instrumentation, 2018, 32(2):97-105.(in Chinese)
[6] KRIZHEVSKY A, SUTSKEVER I, HINTON G E.ImageNet classification with deep convolutional neural networks[C]//Proceedings of Advances in Neural Information Processing Systems.Berlin, Germany:Springer, 2012:1097-1105.
[7] 张小瑞, 陈旋, 孙伟, 等.基于深度学习的车辆再识别研究进展[J].计算机工程, 2020, 46(11):1-11. ZHANG X R, CHEN X, SUN W, et al.Progress of vehicle re-identification research based on deep learning[J].Computer Engineering, 2020, 46(11):1-11.(in Chinese)
[8] 孙福权, 丛成龙, 张琨, 等.基于多模型卷积神经网络的乳腺癌病理医疗图像良恶性诊断[J].小型微型计算机系统, 2020, 41(4):732-735. SUN F Q, CONG C L, ZHANG K, et al.Benign and malignant diagnosis of breast cancer histopathological image based on multi-model neural network[J].Journal of Chinese Computer Systems, 2020, 41(4):732-735.(in Chinese)
[9] OORD A, DIELEMAN S, ZEN H, et al.WaveNet:a generative model for raw audio[EB/OL].(2016-09-19)[2020-04-10].https://arxiv.org/pdf/1609.03499.pdf.
[10] DAUPHIN Y N, FAN A, AULI M, et al.Language modeling with gated convolutional networks[C]//Proceedings of the 34th International Conference on Machine Learning.New York, USA:ACM Press, 2017:933-941.
[11] GEHRING J, AULI M, GRANGIER D, et al.Convolutional sequence to sequence learning[C]//Proceedings of the 34th International Conference on Machine Learning.New York, USA:ACM Press, 2017:1243-1252.
[12] KALCHBRENNER N, ESPEHOLT L, SIMONYAN K, et al.Neural machine translation in linear time[EB/OL].(2017-03-15)[2020-04-10].https://arxiv.org/pdf/1610.10099.pdf.
[13] 李开明, 张群, 罗迎, 等.地面车辆目标识别研究综述[J].电子学报, 2014, 42(3):538-546. LI K M, ZHANG Q, LUO Y, et al.Research review of ground vehicle target recognition[J].Acta Electronica Sinica, 2014, 42(3):538-546.(in Chinese)
[14] 何俊, 张彩庆, 李小珍, 张德海.面向深度学习的多模态融合技术研究综述[J].计算机工程, 2020, 46(5):1-11. HE J, ZHANG C Q, LI X Z, et al.Survey of research on multimodal fusion technology for deep learning[J].Computer Engineering, 2020, 46(5):1-11.(in Chinese)
[15] 罗永恩, 胡继承, 徐茜.基于超图的多模态关联特征处理方法[J].计算机工程, 2017, 43(1):226-230. LUO Y E, HU J C, XU Q, Multimodal correlation feature processing method based on hypergraph[J].Computer Engineering, 2017, 43(1):226-230.(in Chinese)
[16] 董永亮, 柴旭清.基于潜在语义的双层图像-文本多模态检索语义网络[J].计算机工程, 2016, 42(7):299-303, 309. DONG Y L, CHAI X Q.Two-layer image-text semantic network for multi-modal retrieval based on latent semantic[J].Computer Engineering, 2016, 42(7):299-303, 309.(in Chinese)
[17] 韩林轩.基于多模态融合的特种车辆识别的研究与应用[D].开封:河南大学, 2019. HAN L X.Research and application of special vehicle identification based on multi-mode fusion[D].Kaifeng:Henan University, 2019.(in Chinese)
[18] 靳舜.基于声音和震动信号特征融合的车型识别研究[D].西安:长安大学, 2014. JIN S.Research on vehicle model recognition based on sound and vibration signal feature fusion[D].Xi'an:Chang'an University, 2014.(in Chinese)
[19] 吕成戍, 王维国.不均衡数据集下基于SVM的托攻击检测方法[J].计算机工程, 2013, 39(5):132-135. LÜ C S, WANG W G.Shilling attack detection method based on SVM under unbalanced datasets[J].Computer Engineering, 2013, 39(5):132-135.(in Chinese)
[20] LIN T Y, GOYAL P, GIRSHICK R, et al.Focal loss for dense object detection[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2017:2980-2988.
[21] BAI S, KOLTER J Z, KOLTUN V.An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[EB/OL].(2019-09-27)[2020-04-10].https://link.csdn.net/?target=https%3A%2F%2Farxiv.org%2Fpdf%2F1803.01271.pdf.
[22] HE K, ZHANG X, REN S, et al.Deep residual learning for image recognition[C]//Proceedings of 2016 Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:770-778.
[23] HUANG G, LIU Z, MAATEN L, et al.Densely connected convolutional networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2017:4700-4708.

Please choose a citation manager

Content to export