NSSC OpenIR
Dim and Small Target Detection Based on Fully Convolutional Recursive Network
Alternative Title20203709152962;基于全卷积递归网络的弱小目标检测方法
Yang, Qili1,2; Zhou, Binghong1; Zheng, Wei1; Li, Mingtao1
Source PublicationGuangxue Xuebao/Acta Optica Sinica
2020
Volume40Issue:13Pages:1310002
DOI10.3788/AOS202040.1310002
ISSN02532239
Language中文
KeywordConvolution - 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
AbstractThis 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 ByEI ; CSCD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7489
Collection中国科学院国家空间科学中心
Affiliation1.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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