深度学习的空间红外弱小目标状态感知方法

黄乐弘; 曹立华; 李宁; 李毅

doi:10.3788/CO.2019-0120

Volume 13 Issue 3

Jun. 2020

Turn off MathJax

Article Contents

Chinese Optics > 2020 > 13(3): 527-536.

HUANG Le-hong, CAO Li-hua, LI Ning, LI Yi. A state perception method for infrared dim and small targets with deep learning[J]. Chinese Optics, 2020, 13(3): 527-536. doi: 10.3788/CO.2019-0120

Citation:

HUANG Le-hong, CAO Li-hua, LI Ning, LI Yi. A state perception method for infrared dim and small targets with deep learning[J]. Chinese Optics, 2020, 13(3): 527-536. doi: 10.3788/CO.2019-0120

HUANG Le-hong, CAO Li-hua, LI Ning, LI Yi. A state perception method for infrared dim and small targets with deep learning[J]. Chinese Optics, 2020, 13(3): 527-536. doi: 10.3788/CO.2019-0120

Citation:

HUANG Le-hong, CAO Li-hua, LI Ning, LI Yi. A state perception method for infrared dim and small targets with deep learning[J]. Chinese Optics, 2020, 13(3): 527-536. doi: 10.3788/CO.2019-0120

PDF( 5546 KB)

A state perception method for infrared dim and small targets with deep learning

doi: 10.3788/CO.2019-0120

HUANG Le-hong^{1, 2, 3
,},
CAO Li-hua^{1, 3
,
,},
LI Ning^{1, 3
,},
LI Yi^{1, 3
,}

1.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
State Key Laboratory of Laser Interaction with Matter, Changchun 130033, China

Funds: Supported by National Natural Science Foundation of China (No. 61705219)

More Information

Corresponding author: cao0983@sina.com
Received Date: 14 Jun 2019
Rev Recd Date: 12 Aug 2019
Publish Date: 01 Jun 2020

Abstract

Abstract

Aiming at the problems of low accuracy, high artificial interference and high data quality requirements of the current spatial infrared dim target state perception, a new deep learning-based discrimination algorithm is proposed. Firstly, the state change of weak spatial infrared dim target is analyzed and a special data set is established. Then, a convolutional neural network dedicated to target state perception is established and adjustments are made in its local annotations and adaptive threshold. Finally, simulation data is generated from the target's radiation intensity information that was collected in the laboratory and is used to train and test the algorithm. A target state perception evaluation indexing system is established to evaluate the experimental results. The experimental results show that the accuracy of this method is 98.27% when the continuous complete radiation intensity information is inputted. When the radiation intensity information of the segment is inputted, the accuracy of each state is greater than 90%. This algorithm makes up for the shortcomings of current methods, which are not sensitive to low false alarm rates and incomplete target information. It improves detection speed and accuracy and better satisfies the demand for spatial infrared weak target sensing tasks.
- object detection,
- deep learning,
- dim target,
- infrared radiation intensity

FullText(HTML)

References(16)

References

[1]	王建立, 刘欣悦. 智能光学的概念及发展[J]. 中国光学,2013,6(4):437-488. WANG J L, LIU X Y. Concept and development of smart optics[J]. Chinese Optics, 2013, 6(4): 437-488. (in Chinese)
[2]	李宁, 张云峰, 刘春香, 等. 1 m口径红外测量系统的辐射定标[J]. 光学精密工程,2014,22(8):2054-2060. doi: 10.3788/OPE.20142208.2054 LI N, ZHANG Y F, LIU CH X, et al. Calibration of 1 m aperture infrared theodolite[J]. Optics and Precision Engineering, 2014, 22(8): 2054-2060. (in Chinese) doi: 10.3788/OPE.20142208.2054
[3]	许伟琳, 武春风, 逯力红, 等. 基于光谱角时序不变性的红外目标识别[J]. 中国光学,2012,5(3):257-262. XU W L, WU CH F, LU L H, et al. IR target recognition based on invariant sequential spectral angle[J]. Chinese Optics, 2012, 5(3): 257-262. (in Chinese)
[4]	李宁, 杨词银, 曹立华, 等. 3~5 μm红外焦平面阵列的辐射定标[J]. 光学精密工程,2011,19(10):2319-2325. doi: 10.3788/OPE.20111910.2319 LI N, YANG C Y, CAO L H, et al. Radiance calibration for 3~5 μm infrared focal plane array[J]. Optics and Precision Engineering, 2011, 19(10): 2319-2325. (in Chinese) doi: 10.3788/OPE.20111910.2319
[5]	梁华, 宋玉龙, 钱锋, 等. 基于深度学习的航空对地小目标检测[J]. 液晶与显示,2018,33(9):793-800. LIANG H, SONG Y L, QIAN F, et al. Detection of small target in aerial photography based on deep learning[J]. Chinese Journal of Liquid Crystal and Displays, 2018, 33(9): 793-800. (in Chinese)
[6]	陈清江, 张雪, 柴昱洲. 基于卷积神经网络的图像去雾算法[J]. 液晶与显示,2019,34(2):220-227. CHEN Q J, ZHANG X, CHAI Y ZH. Image defogging algorithms based on multiscale convolution neural network[J]. Chinese Journal of Liquid Crystal and Displays, 2019, 34(2): 220-227. (in Chinese)
[7]	周筑博, 高佼, 张巍, 等. 基于深度卷积神经网络的输电线路可见光图像目标检测[J]. 液晶与显示,2018,33(4):317-325. ZHOU ZH B, GAO J, ZHANG W, et al. Object detection of transmission line visual images based on deep convolutional neural network[J]. Chinese Journal of Liquid Crystal and Displays, 2018, 33(4): 317-325. (in Chinese)
[8]	张博, 韩广良. 基于Mask R-CNN的ORB去误匹配方法[J]. 液晶与显示,2018,33(8):690-696. ZHANG B, HAN G L. ORB removal mis-matching method based on Mask R-CNN[J]. Chinese Journal of Liquid Crystal and Displays, 2018, 33(8): 690-696. (in Chinese)
[9]	贠卫国, 史其琦, 王民. 基于深度卷积神经网络的多特征融合的手势识别[J]. 液晶与显示,2019,34(4):417-422. YUN W G, SHI Q Q, WANG M. Multi-feature fusion gesture recognition based on deep convolutional neural network[J]. Chinese Journal of Liquid Crystal and Displays, 2019, 34(4): 417-422. (in Chinese)
[10]	HARE S, GOLODETZ S, SAFFARI A, et al. Struck: structured output tracking with kernels[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10): 2096-2109. doi: 10.1109/TPAMI.2015.2509974
[11]	UIJLINGS J R R, VAN DE SANDE K E A, GEVERS T, et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104(2): 154-171. doi: 10.1007/s11263-013-0620-5
[12]	纪超, 黄新波, 刘慧英, 等. 融合连续区域特性和背景学习模型的显著计算[J]. 模式识别与人工智能,2018,31(4):300-309. JI CH, HUANG X B, LIU H Y, et al. Fusing continuous region characteristics and background learning model for saliency computation[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(4): 300-309. (in Chinese)
[13]	辛鹏, 许悦雷, 唐红, 等. 全卷积网络多层特征融合的飞机快速检测[J]. 光学学报,2018,38(3):0315003. doi: 10.3788/AOS201838.0315003 XIN P, XU Y L, TANG H, et al. Fast airplane detection based on multi-layer feature fusion of fully convolutional networks[J]. Acta Optica Sinica, 2018, 38(3): 0315003. (in Chinese) doi: 10.3788/AOS201838.0315003
[14]	冯小雨, 梅卫, 胡大帅. 基于改进Faster R-CNN的空中目标检测[J]. 光学学报,2018,38(6):0615004. doi: 10.3788/AOS201838.0615004 FENG X Y, MEI W, HU D SH. Aerial target detection based on improved faster R-CNN[J]. Acta Optica Sinica, 2018, 38(6): 0615004. (in Chinese) doi: 10.3788/AOS201838.0615004
[15]	廖祥文, 谢媛媛, 魏晶晶, 等. 基于卷积记忆网络的视角级微博情感分类[J]. 模式识别与人工智能,2018,31(3):219-229. LIAO X W, XIE Y Y, WEI J J, et al. Perspective level microblog sentiment classification based on convolutional memory network[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(3): 219-229. (in Chinese)
[16]	王文秀, 傅雨田, 董峰, 等. 基于深度卷积神经网络的红外船只目标检测方法[J]. 光学学报,2018,38(7):0712006. doi: 10.3788/AOS201838.0712006 WANG W X, FU Y T, DONG F, et al. Infrared ship target detection method based on deep convolution neural network[J]. Acta Optica Sinica, 2018, 38(7): 0712006. (in Chinese) doi: 10.3788/AOS201838.0712006