An ultra-efficient streaming-based FPGA accelerator for infrared target detection

doi:10.11972/j.issn.1001-9014.2022.05.016

	An ultra-efficient streaming-based FPGA accelerator for infrared target detection
	Chen Shao-Yi1,2,3,4 ; Tang Xin-yi 2,3,4; Wang Jian 2,3,4; Huang Jing-Si1,2,3,4 ; Li Zheng 2,3,4
	2022-10
发表期刊	红外与毫米波学报 (IF:0.6[JCR-2023],0.5[5-Year])
ISSN	1001-9014
卷号	41 期号:5 页码:914-922
发表状态	已发表
DOI	10.11972/j.issn.1001-9014.2022.05.016
摘要	Object detection algorithm based on deep learning has achieved great success, significantly better than the effect of traditional algorithms, and even surpassed human in many scenarios. Unlike RGB cameras, infrared cameras can see objects even in the dark, which can be used in many fields like surveillance and autonomous driving. In this paper, a lightweight target detection algorithm for embedded devices is proposed, which is accelerated and deployed using Xilinx Ultrascale+MPSoC FPGA ZU3EG. The accelerator runs at a 350 MHz frequency clock with throughput of 551 FPS and power of only 8. 4 W. The intersection over union (IoU)of the algorithm achieves an accuracy of 73. 6% on FILR datasets. Comparing with the previous work, the accelerator design improves performance by 2. 59× and reduces 49. 02% of the power consumption. © 2022 Chinese Optical Society. All rights reserved.
关键词	Cameras Convolution Convolutional neural networks Deep learning Embedded systems Field programmable gate arrays (FPGA) Interactive computer systems Object detection Security systems Signal detection System-on-chip Thermography (imaging) Convolutional neural network Field programmable gate array Field programmables Infra-red cameras Infrared target detection Object detection algorithms Programmable gate array Real Time system RGB cameras Ultra-efficient
URL	查看原文
收录类别	EI ; 北大核心 ; SCI
语种	英语
资助项目	National Pre-Research Foundation of China[514010405-207]
WOS研究方向	Optics
WOS类目	Optics
WOS记录号	WOS:000911989500016
出版者	Chinese Optical Society
EI入藏号	20224212896047
EI主题词	Real time systems
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 714.2 Semiconductor Devices and Integrated Circuits ; 716.1 Information Theory and Signal Processing ; 721.2 Logic Elements ; 721.3 Computer Circuits ; 722.4 Digital Computers and Systems ; 723.2 Data Processing and Image Processing ; 742.1 Photography ; 742.2 Photographic Equipment ; 914.1 Accidents and Accident Prevention
原始文献类型	Journal article (JA)
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/241066
专题	信息科学与技术学院_硕士生
通讯作者	Li Zheng
作者单位	1.Shanghai Tech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Tech Phys, Shanghai 20083, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Key Lab Infrared Syst Detect & Imaging Technol, Shanghai 200083, Peoples R China
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Chen Shao-Yi,Tang Xin-yi,Wang Jian,et al. An ultra-efficient streaming-based FPGA accelerator for infrared target detection[J]. 红外与毫米波学报,2022,41(5):914-922.
APA	Chen Shao-Yi,Tang Xin-yi,Wang Jian,Huang Jing-Si,&Li Zheng.(2022).An ultra-efficient streaming-based FPGA accelerator for infrared target detection.红外与毫米波学报,41(5),914-922.
MLA	Chen Shao-Yi,et al."An ultra-efficient streaming-based FPGA accelerator for infrared target detection".红外与毫米波学报 41.5(2022):914-922.