Dim and Small Target Detection Based on Fully Convolutional Recursive Network

doi:10.3788/AOS202040.1310002

NSSC OpenIR

	Dim and Small Target Detection Based on Fully Convolutional Recursive Network
Alternative Title	20203709152962;基于全卷积递归网络的弱小目标检测方法
	Yang, Qili 1,2; Zhou, Binghong 1; Zheng, Wei 1; Li, Mingtao 1
Source Publication	Guangxue Xuebao/Acta Optica Sinica
	2020
Volume	40 Issue:13 Pages:1310002
DOI	10.3788/AOS202040.1310002
ISSN	02532239
Language	中文
Keyword	Convolution - Deep learning - Image enhancement - Infrared imaging - Learning systems - Object recognition - Semantics - Signal to noise ratio Background suppression - Background suppression factor - Infrared dim small targets - Probability of detection - Signal to noise ratio gains - Signal-to-clutter ratios - Small target detection - Weak target detection
Abstract	This paper proposes a method for weak target detection based on deep learning. The proposed method based on semantic segmentation uses a fully convolutional recursive network to learn the characteristics of targets in complex backgrounds. Furthermore, it uses residual learning and recursive operation in the network, which exhibits the characteristics of an accelerating network optimization, fewer model parameters, deep recursive supervision, and feature reuse. In two real sequences and other infrared images, the proposed method has achieved the best visual effect in terms of target enhancement and background suppression compared with the three latest detection methods, and it has achieved excellent performance in the probability of detection, signal-to-noise ratio gain, signal-to-clutter ratio gain, and background suppression factor. Therefore, the proposed detection method has good applicability and robustness for infrared dim small target detection in different scenes. © 2020, Chinese Lasers Press. All right reserved.
Indexed By	EI ; CSCD
Citation statistics
Document Type	期刊论文
Identifier	http://ir.nssc.ac.cn/handle/122/7489
Collection	中国科学院国家空间科学中心
Affiliation	1.Laboratory of System Simulation and Concurrent Design Technology, National Space Science Center, Chinese Academy of Sciences, Beijing; 100190, China; 2.College of Engineering and Science, University of Chinese Academy of Sciences, Beijing; 100049, China
Recommended Citation GB/T 7714	Yang, Qili,Zhou, Binghong,Zheng, Wei,et al. Dim and Small Target Detection Based on Fully Convolutional Recursive Network[J]. Guangxue Xuebao/Acta Optica Sinica,2020,40(13):1310002.
APA	Yang, Qili,Zhou, Binghong,Zheng, Wei,&Li, Mingtao.(2020).Dim and Small Target Detection Based on Fully Convolutional Recursive Network.Guangxue Xuebao/Acta Optica Sinica,40(13),1310002.
MLA	Yang, Qili,et al."Dim and Small Target Detection Based on Fully Convolutional Recursive Network".Guangxue Xuebao/Acta Optica Sinica 40.13(2020):1310002.

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