Infrared small target detection using tri-layer window local contrast

被引：0

作者：

Han J. ^{[1
]}

Jiang Y. ^{[1
]}

Zhang X. ^{[1
]}

Liang K. ^{[2
]}

Li Z. ^{[1
]}

Dong X. ^{[1
]}

Li N. ^{[1
]}

机构：

[1] College of Physics and Telecommunication Engineering, Zhoukou Normal University, Zhoukou

[2] School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan

来源：

Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering | 2021年 / 50卷 / 02期

关键词：

Closest filter; IR small target detection; Local contrast; Matched filter; Tri-layer window;

D O I：

10.3788/IRLA20200146

中图分类号：

学科分类号：

摘要：

In infrared (IR) guidance, early warning and other fields, it is of great theoretical significance and application value to detect IR small target with high detection rate, low false alarm rate and high speed. An IR small target detection method based on the tri-layer window local contrast was proposed. The tri-layer window could deal with small targets of different scales by single-scale calculation, so that the detection speed could be accelerated. Meanwhile, the enhancement on the true target and the suppression on the complex background were considered before, during and after the local contrast calculation, so that the detection rate could be improved and the false alarm rate could be reduced. Experiments in some IR sequences and images show that, compared with eight existing algorithms, the proposed algorithm can achieve a better performance on detection rate and false alarm rate, and its average time consumption is only about 1/3 to 1/2 of some multiscale algorithms. Copyright ©2021 Infrared and Laser Engineering. All rights reserved.

引用

共 20 条

[1]

Cui Zheng, Yang Jingli, Li Junbao, Et al., An infrared small target detection framework based on local contrast method, Measurement, 91, pp. 405-413, (2016)

[2]

Fan Mingming, Tian Shaoqing, Liu Kai, Et al., Infrared small target detection algorithm based on gradient direction consistency and eigendecomposition, Infrared and Laser Engineering, 49, 1, (2020)

[3]

Gao Jinyan, Lin Zaiping, An Wei, Infrared small target detection using a temporal variance and spatial patch contrast filter, IEEE Access, 7, pp. 32217-32226, (2019)

[4]

Bai Xiangzhi, Bi Yanguang, Derivative entropy-based contrast measure for infrared small-target detection, IEEE Transactions on Geoscience and Remote Sensing, 56, 4, pp. 2452-2466, (2018)

[5]

Wu Shuangchen, Zuo Zhengrong, Small target detection in infrared images using deep convolutional neural networks, Journal of Infrared and Millimeter Waves, 38, 3, pp. 371-380, (2019)

[6]

Wang Xiaoyang, Peng Zhenming, Zhang Ping, Et al., Infrared small dim target detection based on local contrast combined with region saliency, High Power Laser and Particle Beams, 27, 9, pp. 32-38, (2015)

[7]

Chen Yuwen, Xin Yunhong, An efficient infrared small target detection method based on visual contrast mechanism, IEEE Geosci Remote Sensing Lett, 13, 7, pp. 962-966, (2016)

[8]

Zhang Xiangyue, Ding Qinghai, Luo Haibo, Et al., Infrared dim target detection algorithm based on improved LCM, Infrared and Laser Engineering, 46, 7, (2017)

[9]

Du Peng, Hamdulla A., Infrared moving small-target detection using spatial-temporal local difference measure, IEEE Geoscience and Remote Sensing Letters, 17, 10, pp. 1817-1821, (2020)

[10]

Shao Xiaopeng, Fan Hua, Lu Gangxu, Et al., An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system, Infrared Physics & Technology, 55, 5, pp. 403-408, (2012)

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