首页 > 论文 > 激光与光电子学进展 > 56卷 > 2期(pp:21001--1)

基于卷积神经网络的激光距离选通式成像目标识别

Laser Range-Gated Imaging Target Recognition Based on Convolutional Neural Network

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

为解决距离选通式激光成像中由于图像模糊而导致目标识别率偏低的难题, 提出一种保留特征的卷积神经网络(KFCNN)模型, 用于激光选通图像中的目标识别。与传统的卷积神经网络不同, KFCNN使用一个特征保留层来提高模糊目标的识别率, 提高目标识别的稳健性。为实现特征保留, KFCNN通过增加特征保留约束项及正则化来优化特征保留目标函数并进行训练, 通过减小特征保留目标函数值来保证训练样本在模糊之前和之后的特征映射相一致。实验结果表明, KFCNN改善了因模糊造成识别率降低的问题, 进而提升了距离选通式激光成像中对指定目标的识别率。

Abstract

In order to solve the difficult problem of low target recognition rate caused by image blur in the laser rang-gated imaging process, we propose the keep-feature convolutional neural network (KFCNN) model for the target recognition of laser rang-gated images. Different from the convolutional neural network (CNN), the KFCNN model is used to improve the recognition rate of blurred targets and the robustness of target recognition with a new keep-feature layer. To achieve keep-feature in the KFCNN model, we optimize the keep-feature objective functions and the training by imposing keep-feature constraints and regularization. In addition, the feature maps of training samples are kept consistent before and after image blur when the value of the keep-feature objective function is reduced. The experimental results show that KFCNN improves the problem of recognition rate reduction caused by image blur and further improves the recognition rate of specified targets in laser rang-gated imaging.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391.4

DOI:10.3788/lop56.021001

所属栏目:图像处理

收稿日期:2018-06-22

修改稿日期:2018-06-25

网络出版日期:2018-07-26

作者单位    点击查看

王书宇:陆军炮兵防空兵学院, 安徽 合肥 230031
陶声祥:陆军炮兵防空兵学院, 安徽 合肥 230031
杨钒:陆军炮兵防空兵学院, 安徽 合肥 230031
艾磊:陆军炮兵防空兵学院, 安徽 合肥 230031

联系人作者:王书宇(1512264822@qq.com)

【1】Gztepe K. Automatic target recognition on land using three dimensional (3D) laser radar and artificial neural networks[J]. The South African Journal of Industrial Engineering, 2013, 24(1): 107-120.

【2】Zhao J C, Wang D N, Chen C Q, et al. Infrared laser active imaging and recognition technology[J]. Chinese Optics , 2013, 6(5): 795-802.
赵建川, 王弟男, 陈长青, 等. 红外激光主动成像和识别[J]. 中国光学, 2013, 6(5): 795-802.

【3】Wang C J, Sun T, Shi N N, et al. Laser active imaging and recognition system based on double hidden layer BP algorithm[J]. Optics and Precision Engineering, 2014, 22(6): 1639-1647.
王灿进, 孙涛, 石宁宁, 等. 基于双隐含层BP算法的激光主动成像识别系统[J]. 光学 精密工程, 2014, 22(6): 1639-1647.

【4】Li Y C, Fan Y C. 3-D target recognition algorithm of laser image based on rough set and neural network[J]. Laserand Infrared, 2014, 44(6): 676-681.
李迎春, 范有臣. 一种创新性激光图像三维目标识别算法[J]. 激光与红外, 2014, 44(6): 676-681.

【5】Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

【6】Liu D W, Han L, Han X Y. High spatial resolution remote sensing image classification based on deep learning[J]. Acta Optica Sinica, 2016, 36(4): 0428001.
刘大伟, 韩玲, 韩晓勇. 基于深度学习的高分辨率遥感影像分类研究[J]. 光学学报, 2016, 36(4): 0428001.

【7】Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J/OL]. 2014[2018-05-25]. https:∥arxiv.org/abs/1409.1556.

【8】Liu F, Shen T S, Ma X X. Convolutional neural network based multi-band ship target recognition with feature fusion[J]. Acta Optica Sinica, 2017, 37(10): 1015002.
刘峰, 沈同圣, 马新星. 特征融合的卷积神经网络多波段舰船目标识别[J].光学学报, 2017, 37(10): 1015002.

【9】Schroff F, Kalenichenko D, Philbin J. Facenet: a unified embedding for face recognition and clustering[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 815-823.

【10】Ye G L, Sun S Y, Gao K J, et al. Night time pedestrian detection based on faster region convolution neural network[J]. Laser & Optoelectronics Progress, 2017, 54(8): 081003.
叶国林, 孙韶媛, 高凯珺, 等. 基于加速区域卷积神经网络的夜间行人检测研究[J]. 激光与光电子学进展, 2017, 54(8): 081003.

【11】Taigman Y, Yang M, Ranzato M A, et al. Deepface: Closing the gap to human-level performance in face verification[C]∥Deepface: Closing the gap to human-level performance in face verification, June 23-28, 2014 Columbus, OH, USA. New York: IEEE, 2014: 1701-1708.

【12】Wang J R, Yuan C. Facial expression recognition with multi-scale convolution neural network[C]∥Chen E, Gong Y, Tie Y. Advances in Multimedia Information Processing: PCM 2016. Cham: Springer, 2016: 376-385.

【13】Richardson W H. Bayesian-based iterative method of image restoration[J]. Journal of the Optical Society of America A, 1972, 62(1): 55-59.

【14】Ojansivu V, Heikkil J. Blur insensitive texture classification using local phase quantization[C]∥Elmoataz A, Lezoray O, Nouboud F, et al. Image and Signal Processing: ICISP 2008. Berlin, Heidelberg: Springer, 2008: 236-243.

【15】Chen Y, Fan R S, Wang J X, et al. High resolution image classification method combining with minimum noise fraction rotation and convolution neural network[J]. Laser & Optoelectronics Progress, 2017, 54(10): 102801.
陈洋, 范荣双, 王竞雪, 等. 结合最小噪声分离变换和卷积神经网络的高分辨影像分类方法[J]. 激光与光电子学进展, 2017, 54(10): 102801.

【16】Yu D, Deng L. Deep learning and its applications to signal and information processing[exploratory dsp][J]. IEEE Signal Processing Magazine, 2011, 28(1): 145-154.

【17】Ji S W, Xu W, Yang M, et al. 3D convolutional neural networks for human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 221-231.

【18】Hu F, Xia G S, Hu J W, et al. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery[J]. Remote Sensing, 2015, 7(11): 14680-14707.

引用该论文

Wang Shuyu,Tao Shengxiang,Yang Fan,Ai Lei. Laser Range-Gated Imaging Target Recognition Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(2): 021001

王书宇,陶声祥,杨钒,艾磊. 基于卷积神经网络的激光距离选通式成像目标识别[J]. 激光与光电子学进展, 2019, 56(2): 021001

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF