Infrared small target segmentation networks: A survey

被引:115
作者
Kou, Renke [1 ]
Wang, Chunping [1 ]
Peng, Zhenming [2 ]
Zhao, Zhihe [3 ]
Chen, Yaohong [4 ]
Han, Jinhui [5 ]
Huang, Fuyu [1 ]
Yu, Ying [1 ]
Fu, Qiang [1 ]
机构
[1] Army Engn Univ, Shijiazhuang Campus, Shijiazhuang, Peoples R China
[2] Univ Elect Sci & Technol, Sch Informat & Commun Engn, Chengdu, Peoples R China
[3] Air Force Med Univ, Dept Craniofacial Plast & Aesthet Surg, Affiliated Hosp 3, Xian, Peoples R China
[4] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Peoples R China
[5] Zhoukou Normal Univ, Coll Phys & Telecommun Engn, Zhoukou, Peoples R China
基金
中国国家自然科学基金;
关键词
Infrared small target; Characteristic analysis; Segmentation network; Deep learning; Collaborative technology; Data-driven; False alarm; Missed detection;
D O I
10.1016/j.patcog.2023.109788
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Fast and robust small target detection is one of the key technologies in the infrared (IR) search and tracking systems. With the development of deep learning, there are many data-driven IR small target segmentation algorithms, but they have not been extensively surveyed; we believe our proposed survey is the first to systematically survey them. Focusing on IR small target segmentation tasks, we summarized 7 characteristics of IR small targets, 3 feature extraction methods, 8 design strategies, 30 segmentation networks, 8 loss functions, and 13 evaluation indexes. Then, the accuracy, robustness, and computational complexities of 18 segmentation networks on 5 public datasets were compared and analyzed. Finally, we have discussed the existing problems and future trends in the field of IR small target detection. The proposed survey is a valuable reference for both beginners adapting to current trends in IR small target detection and researchers already experienced in this field.& COPY; 2023 Elsevier Ltd. All rights reserved.
引用
收藏
页数:25
相关论文
共 91 条
[1]   SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation [J].
Badrinarayanan, Vijay ;
Kendall, Alex ;
Cipolla, Roberto .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 39 (12) :2481-2495
[2]  
Chaurasia A, 2017, 2017 IEEE VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP)
[3]   A Local Contrast Method for Small Infrared Target Detection [J].
Chen, C. L. Philip ;
Li, Hong ;
Wei, Yantao ;
Xia, Tian ;
Tang, Yuan Yan .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (01) :574-581
[4]   Local Patch Network With Global Attention for Infrared Small Target Detection [J].
Chen, Fang ;
Gao, Chenqiang ;
Liu, Fangcen ;
Zhao, Yue ;
Zhou, Yuxi ;
Meng, Deyu ;
Zuo, Wangmeng .
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2022, 58 (05) :3979-3991
[5]  
Chen LC, 2017, Arxiv, DOI [arXiv:1706.05587, 10.48550/arXiv.1706.05587]
[6]   Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation [J].
Chen, Liang-Chieh ;
Zhu, Yukun ;
Papandreou, George ;
Schroff, Florian ;
Adam, Hartwig .
COMPUTER VISION - ECCV 2018, PT VII, 2018, 11211 :833-851
[7]   DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [J].
Chen, Liang-Chieh ;
Papandreou, George ;
Kokkinos, Iasonas ;
Murphy, Kevin ;
Yuille, Alan L. .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (04) :834-848
[8]   A Multi-Task Framework for Infrared Small Target Detection and Segmentation [J].
Chen, Yuhang ;
Li, Liyuan ;
Liu, Xin ;
Su, Xiaofeng .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
[9]   Xception: Deep Learning with Depthwise Separable Convolutions [J].
Chollet, Francois .
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, :1800-1807
[10]   Asymmetric Contextual Modulation for Infrared Small Target Detection [J].
Dai, Yimian ;
Wu, Yiquan ;
Zhou, Fei ;
Barnard, Kobus .
2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2021), 2021, :949-958