中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
A Robust Infrared Small Target Detection Method Jointing Multiple Information and Noise Prediction: Algorithm and Benchmark

文献类型：期刊论文

作者Meng, Siqiang1,3; Zhang, Congxuan1,3; Shi, Qi1,3; Chen, Zhen1,3; Hu, Weiming2,3; Lu, Feng1,3
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2023
卷号61页码:17
关键词Dataset infrared small target detection (IRSTD) multiple information noise prediction regional positioning
ISSN号0196-2892
DOI10.1109/TGRS.2023.3295932
通讯作者Zhang, Congxuan(zcxdsg@163.com) ; Chen, Zhen(dr_chenzhen@163.com)
英文摘要Infrared small target detection (IRSTD) plays an important role in many military and civilian applications. Despite the great advances made by IRSTD studies in recent years, most of the existing methods have difficulty in balancing detection probabilities and false alarms. Moreover, there are only a few public datasets for infrared small targets, which limits the development of IRSTD research. To address the abovementioned issues, in this article, we propose a robust IRSTD method that joins multiple pieces of information and noise predictions, named MINP-Net. Specifically, we first design a gradient and contextual information extraction module to extract multiscale features from an input infrared image. Second, we construct a noise prediction network to model the background noise. Third, we plan a regional positioning branch to provide a coarse target location to decrease the false alarm ratio. In addition, we build a new IRSTD benchmark to advance the research in this field, named the NCHU-Seg dataset. To the best of the authors' knowledge, the NCHU-Seg dataset is the largest real-world scene dataset for evaluating infrared small target segmentation methods. For a comprehensive evaluation, we compare our method with some of the state-of-the-art methods on both the well-known NUAA-SIRST dataset and our NCHU-Seg dataset. The experimental results demonstrate that the proposed MINP-Net method performs better in terms of detection effectiveness and segmentation accuracy and effectively balances the detection probabilities and false alarms with complex backgrounds. (The code and dataset are available at https://github.com/PCwenyue.)
WOS关键词LOCAL CONTRAST METHOD ; IMAGE
资助项目National Key Research and Development Program of China[2020YFC2003800] ; National Natural Science Foundation of China[62222206] ; National Natural Science Foundation of China[62272209] ; National Natural Science Foundation of China[61866026] ; National Natural Science Foundation of China[61866025] ; National Natural Science Foundation of Jiangxi Province[20202ACB214007] ; Technology Innovation Guidance Program of Jiangxi Province[20212AEI91005] ; Advantage Subject Team Project of Jiangxi Province[20165BCB19007]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001040071000020
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of Jiangxi Province ; Technology Innovation Guidance Program of Jiangxi Province ; Advantage Subject Team Project of Jiangxi Province
源URL[http://ir.ia.ac.cn/handle/173211/53930]  
专题多模态人工智能系统全国重点实验室
通讯作者Zhang, Congxuan; Chen, Zhen
作者单位1.Nanchang Hangkong Univ, Key Lab Nondestruct Testing, Minist Educ, Nanchang 330063, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
3.Nanchang Hangkong Univ, Sch Measuring & Opt Engn, Nanchang 330063, Peoples R China
推荐引用方式
GB/T 7714		 Meng, Siqiang,Zhang, Congxuan,Shi, Qi,et al. A Robust Infrared Small Target Detection Method Jointing Multiple Information and Noise Prediction: Algorithm and Benchmark[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2023,61:17.
APA Meng, Siqiang,Zhang, Congxuan,Shi, Qi,Chen, Zhen,Hu, Weiming,&Lu, Feng.(2023).A Robust Infrared Small Target Detection Method Jointing Multiple Information and Noise Prediction: Algorithm and Benchmark.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,61,17.
MLA Meng, Siqiang,et al."A Robust Infrared Small Target Detection Method Jointing Multiple Information and Noise Prediction: Algorithm and Benchmark".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61(2023):17.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。