Ship Target Recognition Based on Highly Efficient Scalable Improved Residual Structure Neural Network

Zhequan FU; Shangsheng LI; Xiangping LI; Bo DAN; Xukun WANG

doi:10.11999/JEIT190913

Volume 42 Issue 12

Dec. 2020

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(12): 3005-3012

Zhequan FU, Shangsheng LI, Xiangping LI, Bo DAN, Xukun WANG. Ship Target Recognition Based on Highly Efficient Scalable Improved Residual Structure Neural Network[J]. Journal of Electronics & Information Technology, 2020, 42(12): 3005-3012. doi: 10.11999/JEIT190913

Citation:

Zhequan FU, Shangsheng LI, Xiangping LI, Bo DAN, Xukun WANG. Ship Target Recognition Based on Highly Efficient Scalable Improved Residual Structure Neural Network[J]. Journal of Electronics & Information Technology, 2020, 42(12): 3005-3012. doi: 10.11999/JEIT190913

Citation:

PDF( 2751 KB)

Ship Target Recognition Based on Highly Efficient Scalable Improved Residual Structure Neural Network

doi: 10.11999/JEIT190913

Naval Aviation University, Yantai 264001, China

Received Date: 2019-11-14
Rev Recd Date: 2020-04-16

Available Online: 2020-04-25

Publish Date: 2020-12-08

Abstract

Abstract

The depth of neural network is positively correlated with the recognition effect in a certain range. In order to solve the problem that model recognition accuracy decreases when the number of network layers increases after exceeding the range. A neural network model with efficient micro internal blocks structure and residual network structure is proposed, which is used for recognition of ship targets based on High Range Resolution Profile (HRRP) data. In this method, the convolution module with a small scale convolution kernel is used to extract automatically the stable and separable features of target. And the intra-class distance of the target is constrained by the joint loss function to improve the recognition ability. Simulation results show that compared with other common network structures, this model has better recognition performance and stronger noise robustness with fewer model parameters.
- Target recognition,
- High Range Resolution Profile (HRRP),
- Neural Network (NN),
- Residual Structure

FullText(HTML)

References(18)

References

魏存伟, 段发阶, 刘先康. 基于宽带雷达HRRP舰船目标长度估计算法[J]. 系统工程与电子技术, 2018, 40(9): 1960–1965. doi: 10.3969/j.issn.1001-506X.2018.09.10

WEI Cunwei, DUAN Fajie, and LIU Xiankang. Length estimation method of ship target based on wide-band radar’s HRRP[J]. Systems Engineering and Electronics, 2018, 40(9): 1960–1965. doi: 10.3969/j.issn.1001-506X.2018.09.10

贺思三, 赵会宁, 张永顺. 基于时频域联合滤波的中段群目标信号分离[J]. 雷达学报, 2015, 4(5): 545–551. doi: 10.12000/JR15008

HE Sisan, ZHAO Huining, and ZHANG Yongshun. Signal separation for target group in midcourse based on time-frequency filtering[J]. Journal of Radars, 2015, 4(5): 545–551. doi: 10.12000/JR15008

吴佳妮, 陈永光, 代大海, 等. 基于快速密度搜索聚类算法的极化HRRP分类方法[J]. 电子与信息学报, 2016, 38(10): 2461–2467. doi: 10.11999/JEIT151457

WU Jiani, CHEN Yongguang, DAI Dahai, et al. Target recognition for polarimetric HRRP based on fast density search clustering method[J]. Journal of Electronics &Information Technology, 2016, 38(10): 2461–2467. doi: 10.11999/JEIT151457

李建伟, 曲长文, 彭书娟, 等. 基于生成对抗网络和线上难例挖掘的SAR图像舰船目标检测[J]. 电子与信息学报, 2019, 41(1): 143–149. doi: 10.11999/JEIT180050

LI Jianwei, QU Changwen, PENG Shujuan, et al. Ship detection in SAR images based on generative adversarial network and online hard examples mining[J]. Journal of Electronics &Information Technology, 2019, 41(1): 143–149. doi: 10.11999/JEIT180050

杜兰, 魏迪, 李璐, 等. 基于半监督学习的SAR目标检测网络[J]. 电子与信息学报, 2020, 42(1): 154–163. doi: 10.11999/JEIT190783

DU Lan, WEI Di, LI Lu, et al. SAR target detection network via semi-supervised learning[J]. Journal of Electronics &Information Technology, 2020, 42(1): 154–163. doi: 10.11999/JEIT190783

罗会兰, 卢飞, 孔繁胜. 基于区域与深度残差网络的图像语义分割[J]. 电子与信息学报, 2019, 41(11): 2777–2786. doi: 10.11999/JEIT190056

LUO Huilan, LU Fei, and KONG Fansheng. Image semantic segmentation based on region and deep residual network[J]. Journal of Electronics &Information Technology, 2019, 41(11): 2777–2786. doi: 10.11999/JEIT190056

XING Shihong and ZHANG Shaokang. Ship model recognition based on convolutional neural networks[C]. 2018 IEEE International Conference on Mechatronics and Automation, Changchun, China, 2018: 144-148. doi: 10.1109/ICMA.2018.8484362.

杨宏宇, 王峰岩. 基于深度卷积神经网络的气象雷达噪声图像语义分割方法[J]. 电子与信息学报, 2019, 41(10): 2373–2381. doi: 10.11999/JEIT190098

YANG Hongyu and WANG Fengyan. Meteorological radar noise image semantic segmentation method based on deep convolutional neural network[J]. Journal of Electronics &Information Technology, 2019, 41(10): 2373–2381. doi: 10.11999/JEIT190098

王鑫, 李可, 宁晨, 等. 基于深度卷积神经网络和多核学习的遥感图像分类方法[J]. 电子与信息学报, 2019, 41(5): 1098–1105. doi: 10.11999/JEIT180628

WANG Xin, LI Ke, NING Chen, et al. Remote sensing image classification method based on deep convolution neural network and multi-kernel learning[J]. Journal of Electronics &Information Technology, 2019, 41(5): 1098–1105. doi: 10.11999/JEIT180628

郭晨, 简涛, 徐从安, 等. 基于深度多尺度一维卷积神经网络的雷达舰船目标识别[J]. 电子与信息学报, 2019, 41(6): 1302–1309. doi: 10.11999/JEIT180677

GUO Chen, JIAN Tao, XU Congan, et al. Radar HRRP target recognition based on deep multi-scale 1D convolutional neural network[J]. Journal of Electronics &Information Technology, 2019, 41(6): 1302–1309. doi: 10.11999/JEIT180677

王容川, 庄志洪, 王宏波, 等. 基于卷积神经网络的雷达目标HRRP分类识别方法[J]. 现代雷达, 2019, 41(5): 33–38. doi: 10.16592/j.cnki.1004-7859.2019.05.007

WANG Rongchuan, ZHUANG Zhihong, WANG Hongbo, et al. HRRP classification and recognition method of radar target based on convolutional neural network[J]. Modern Radar, 2019, 41(5): 33–38. doi: 10.16592/j.cnki.1004-7859.2019.05.007

刘兴旺. 一种多层预训练卷积神经网络在图像识别中的应用[D]. [硕士论文], 中南民族大学, 2018.

LIU Xingwang. The application of a multi-layers pre-training convolutional neural network in image recognition[D]. [Master dissertation], South-Central University for Nationalities, 2018.

赵飞翔, 刘永祥, 霍凯. 基于栈式降噪稀疏自动编码器的雷达目标识别方法[J]. 雷达学报, 2017, 6(2): 149–156. doi: 10.12000/JR16151

ZHAO Feixiang, LIU Yongxiang, and HUO Kai. Radar target recognition based on stacked denoising sparse autoencoder[J]. Journal of Radars, 2017, 6(2): 149–156. doi: 10.12000/JR16151

KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. ImageNet classification with deep convolutional neural networks[C]. The 25th International Conference on Neural Information Processing Systems, Red Hook, United States, 2012: 1097–1105.

SIMONYAN K and ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]. The 3rd International Conference on Learning Representations, San Diego, United States, 2015: 1–14.

HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, United States, 2016: 770–778. doi: 10.1109/CVPR.2016.90.

SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[C]. The 31st AAAI Conference on Artificial Intelligence, San Francisco, United States, 2017: 4278–4284.

WEN Yandong, ZHANG Kaipeng, LI Zhifeng, et al. A discriminative feature learning approach for deep face recognition[C]. The 14th European Conference on Computer Vision, Amsterdam, Netherlands, 2016: 499–515. doi: 10.1007/978-3-319-46478-7_31.

Relative Articles

Supplements(0)

Cited By

Proportional views