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

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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
Accepted Date:06 Jan 2022
Rev Recd Date:28 Oct 2021

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

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References (19)

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