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

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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

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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)

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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

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References

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