Mar. 2022

Article Contents

Chinese Optics> 2022> 15(2): 267-275.

ZHAO Peng-peng, LI Shu-zhong, LI Xun, LUO Jun, CHANG Kai. Infrared dim small target detection based on visual saliency and local entropy[J]. Chinese Optics, 2022, 15(2): 267-275. doi: 10.37188/CO.2021-0170

Citation:

ZHAO Peng-peng, LI Shu-zhong, LI Xun, LUO Jun, CHANG Kai. Infrared dim small target detection based on visual saliency and local entropy[J].Chinese Optics, 2022, 15(2): 267-275.doi:10.37188/CO.2021-0170

Citation:

ZHAO Peng-peng, LI Shu-zhong, LI Xun, LUO Jun, CHANG Kai. Infrared dim small target detection based on visual saliency and local entropy[J].Chinese Optics, 2022, 15(2): 267-275.doi:10.37188/CO.2021-0170

PDF( 3226 KB)

Infrared dim small target detection based on visual saliency and local entropy

doi:10.37188/CO.2021-0170

1.
Naval Research Institute, Beijing 100036, China
2.
Northern Institute of Electronic Equipment, Beijing 100036, China

Funds:Supported by the National Natural Science Foundation of China（No. 62101589）

More Information

Corresponding author:kerkai@163.com
Received Date:13 Sep 2021
Rev Recd Date:28 Oct 2021
Accepted Date:06 Jan 2022

Available Online:08 Jan 2022

Publish Date:21 Mar 2022

Abstract

Abstract

To improve the high false-alarm rate and poor real-time capability in detecting infrared small dim targets, a novel algorithm based on visual saliency and local entropy is proposed in this paper. This method solves the problem from coarse to fine detecting of small targets. First, a local entropy method is used to obtain the region of interest. Then, an improved visual saliency method is used to calculate local contrast. Finally, a threshold segmentation method is used to extract dim infrared small targets. The method is verified using a contrast test with TOPHAT and LCM, and the results show that the performance of this method precedes the TOPHAT algorithm and LCM algorithm. The false alarm rate by this method decreases to 62.5% and 33.3% compared with the other two algorithms, and the time cost decrease to 38.6% of that of LCM. The method can achieve accurate detection of infrared dim and small targets in a complicated environment, solving the high false alarm rate and poor real-time capability issues to some extent.
- visual saliency,
- infrared images,
- dim small target detection,
- local entropy

FullText(HTML)

References (19)

References

[1]	黄乐弘, 曹立华, 李宁, 等. 深度学习的空间红外弱小目标状态感知方法[J]. 中国光学,2020,13(3):527-536. HUANG L H, CAO L H, LI N, et al. A state perception method for infrared dim and small targets with deep learning[J]. Chinese Optics, 2020, 13(3): 527-536. (in Chinese)
[2]	任向阳, 王杰, 马天磊, 等. 红外弱小目标检测技术综述[J]. 郑州大学学报(理学版),2020,52(2):1-21. REN X Y, WANG J, MA T L, et al. Review on infrared dim and small target detection technology[J]. Journal of Zhengzhou University( Natural Science Edition) , 2020, 52(2): 1-21. (in Chinese)
[3]	马铭阳, 王德江, 孙翯, 等. 基于稳健主成分分析和多点恒虚警的红外弱小目标检测[J]. 光学学报,2019,39(8):0810001. doi:10.3788/AOS201939.0810001 MA M Y, WANG D J, SUN H, et al. Infrared dim-small target detection based on robust principal component analysis and multi-point constant false alarm[J]. Acta Optica Sinica, 2019, 39(8): 0810001. (in Chinese) doi:10.3788/AOS201939.0810001
[4]	周苑, 张健民, 林晓. 基于加权LoG算子的红外弱小目标检测方法研究[J]. 应用光学,2017,38(1):114-119. ZHOU Y, ZHANG J M, LIN X. Infrared small target detection using weighting LoG operator[J]. Journal of Applied Optics, 2017, 38(1): 114-119. (in Chinese)
[5]	魏然然, 詹伟达, 朱德鹏, 等. 改进多尺度的Retinex红外图像增强[J]. 液晶与显示,2021,36(3):465-474. doi:10.37188/CJLCD.2020-0109 WEI R R, ZHAN W D, ZHU D P, et al. Improved multi-scale Retinex infrared image enhancement[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(3): 465-474. (in Chinese) doi:10.37188/CJLCD.2020-0109
[6]	MAO X, DIAO W H. Criterion to evaluate the quality of infrared small target images[J]. Journal of Infrared, Millimeter, and Terahertz Waves, 2009, 30(1): 56-64. doi:10.1007/s10762-008-9410-5
[7]	王刚, 陈永光, 杨锁昌, 等. 采用图像块对比特性的红外弱小目标检测[J]. 光学精密工程,2015,23(5):1424-1433. doi:10.3788/OPE.20152305.1424 WANG G, CHEN Y G, YANG S CH, et al. Detection of infrared dim small target based on image patch contrast[J]. Optics and Precision Engineering, 2015, 23(5): 1424-1433. (in Chinese) doi:10.3788/OPE.20152305.1424
[8]	何耀民, 何华锋, 徐永壮, 等. 基于改进小波变换的海上目标检测[J]. 系统工程与电子技术,2020,42(1):83-89. doi:10.3969/j.issn.1001-506X.2020.01.12 HE Y M, HE H F, XU Y ZH, et al. Marine target detection based on improved wavelet transform[J]. Systems Engineering and Electronics, 2020, 42(1): 83-89. (in Chinese) doi:10.3969/j.issn.1001-506X.2020.01.12
[9]	DIRAMI A, HAMMOUCHE K, DIAF M, et al. Fast multilevel thresholding for image segmentation through a multiphase level set method[J]. Signal Processing, 2013, 93(1): 139-153. doi:10.1016/j.sigpro.2012.07.010
[10]	SUCCARY R, KALMANOVITCH H, SHURNIK Y, et al. Point target detection[J]. Proceedings of SPIE, 2003, 4820: 671-675. doi:10.1117/12.453556
[11]	强勇, 焦李成, 保铮. 动态规划算法进行弱目标检测的机理研究[J]. 电子与信息学报,2003,25(6):721-727. QIANG Y, JIAO L CH, BAO ZH. Study on mechanism of dynamic programming algorithm for dim target detection[J]. Journal of Electronics and Information Technology, 2003, 25(6): 721-727. (in Chinese)
[12]	杨帆, 汪文英, 王茹琪. 基于粒子滤波TBD的高机动目标检测技术[J]. 中国电子科学研究院学报,2018,13(3):279-283. doi:10.3969/j.issn.1673-5692.2018.03.008 YANG F, WANG W Y, WANG R Q. High maneuvering target detection technology based on particle filter TBD[J]. Journal of China Academy of Electronics and Information Technology, 2018, 13(3): 279-283. (in Chinese) doi:10.3969/j.issn.1673-5692.2018.03.008
[13]	SERENCES J T, YANTIS S. Selective visual attention and perceptual coherence[J]. Trends in Cognitive Sciences, 2006, 10(1): 38-45. doi:10.1016/j.tics.2005.11.008
[14]	ROLLS E T, DECO G. Attention in natural scenes: neurophysiological and computational bases[J]. Neural Networks, 2006, 19(9): 1383-1394. doi:10.1016/j.neunet.2006.08.007
[15]	刘杨帆, 曹立华, 李宁, 等. 基于YOLOv4的空间红外弱目标检测[J]. 液晶与显示,2021,36(4):615-623. doi:10.37188/CJLCD.2020-0227 LIU Y F, CAO L H, LI N, et al. Detection of space infrared weak target based on YOLOv4[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(4): 615-623. (in Chinese) doi:10.37188/CJLCD.2020-0227
[16]	陈诗媛, 廖一鹏, 张进, 等. 结合NSST显著性检测及图割的泡沫红外图像分割[J]. 液晶与显示,2021,36(4):584-595. doi:10.37188/CJLCD.2020-0234 CHEN SH Y, LIAO Y P, ZHANG J, et al. Foam infrared image segmentation combining NSST saliency detection and graph cuts[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(4): 584-595. (in Chinese) doi:10.37188/CJLCD.2020-0234
[17]	ITTI L, KOCH C, NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259. doi:10.1109/34.730558
[18]	CHEN C L P, LI H, WEI Y T, et al. A local contrast method for small infrared target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 574-581. doi:10.1109/TGRS.2013.2242477
[19]	黄果, 许黎, 陈庆利, 等. 非局部多尺度分数阶微分图像增强算法研究[J]. 电子与信息学报,2019,41(12):2972-2979. doi:10.11999/JEIT190032 HUANG G, XU L, CHEN Q L, et al. Research on non-local multi-scale fractional differential image enhancement algorithm[J]. Journal of Electronics& Information Technology, 2019, 41(12): 2972-2979. (in Chinese) doi:10.11999/JEIT190032