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A novel hardware-oriented ultra-high-speed object detection algorithm based on convolutional neural network

文献类型：期刊论文

作者Li, Jianquan1,2; Long, Xianlei1,2; Hu, Shenhua1,2; Hu, Yiming1,2; Gu, Qingyi1,2; Xu, De1,2
刊名JOURNAL OF REAL-TIME IMAGE PROCESSING
出版日期2019-12-21
页码12
ISSN号1861-8200
关键词FPGA implementation High-speed vision Fast-object detection Convolutional neural network
DOI10.1007/s11554-019-00931-5
通讯作者Gu, Qingyi(qingyi.gu@ia.ac.cn)
英文摘要This paper describes a hardware-oriented two-stage algorithm that can be deployed in a resource-limited field-programmable gate array (FPGA) for fast-object detection and recognition with out external memory. The first stage is the bounding boxes proposal with a conventional object detection method, and the second is convolutional neural network (CNN)-based classification for accuracy improvement. Frequently accessing external memories significantly affects the execution efficiency of object classification. Unfortunately, the existing CNN models with a large number of parameters are difficult to deploy in FPGAs with limited on-chip memory resources. In this study, we designed a compact CNN model and performed the hardware-oriented quantization for parameters and intermediate results. As a result, CNN-based ultra-fast-object classification was realized with all parameters and intermediate results stored on chip. Several evaluations were performed to demonstrate the performance of the proposed algorithm. The object classification module consumes only 163.67 Kbits of on-chip memories for ten regions of interest (ROIs), this is suitable for low-end FPGA devices. In the aspect of accuracy, our method provides a correctness rate of 98.01% in open-source data set MNIST and over 96.5% in other three self-built data sets, which is distinctly better than conventional ultra-high-speed object detection algorithms.
资助项目National Natural Science Foundation of China[61673376]
WOS研究方向Computer Science ; Engineering ; Imaging Science & Photographic Technology
语种英语
出版者SPRINGER HEIDELBERG
WOS记录号WOS:000541768000001
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/39942]  
专题精密感知与控制研究中心_精密感知与控制
中国科学院自动化研究所
通讯作者Gu, Qingyi
作者单位1.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Li, Jianquan,Long, Xianlei,Hu, Shenhua,et al. A novel hardware-oriented ultra-high-speed object detection algorithm based on convolutional neural network[J]. JOURNAL OF REAL-TIME IMAGE PROCESSING,2019:12.
APA Li, Jianquan,Long, Xianlei,Hu, Shenhua,Hu, Yiming,Gu, Qingyi,&Xu, De.(2019).A novel hardware-oriented ultra-high-speed object detection algorithm based on convolutional neural network.JOURNAL OF REAL-TIME IMAGE PROCESSING,12.
MLA Li, Jianquan,et al."A novel hardware-oriented ultra-high-speed object detection algorithm based on convolutional neural network".JOURNAL OF REAL-TIME IMAGE PROCESSING (2019):12.

入库方式： OAI收割

来源：自动化研究所

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