PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration

doi:10.1109/ACCESS.2019.2938106

	PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration
	Li, Lei 1; Li, Yong 2,3; Lian, Kechao 1; Bian, Xiaoyu 1; Yang, Kuan 1; Tian, Yongzhi 1
	2019
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	7 页码:123280-123288
摘要	Automatic digital pressure gauge calibration is challenging due to various unconstrained conditions. Although existing CNN-RNN based methods have been almost perfect on scene text recognition, they fail to perform well on digital pressure gauge calibration that requires to be extremely computation-efficient and accurate. In this paper, we propose a light weight fully convolutional sequence recognition network for fast and accurate digital Pressure Gauge Calibration (PGC-Net). PGC-Net integrates feature extraction, sequence modelling and transcription into a unified framework. Experimental results show that PGC-Net runs 28 fps on CPU with 97.41% accuracy. Compared with previous methods, PGC-Net achieves better or comparable performance at lower inference time. Without bells and whistles, PGC-Net is capable of recognizing decimal points that usually appear in pressure gauge images, which evidently verifies the feasibility of PGC-Net. We collected a dataset that contains 17, 240 gauge images with annotated labels for automatic digital pressure gauge calibration. The dataset has been public for future research.
关键词	Digital pressure gauge calibration automatic meter reading sequence text recognition light weight CNN digital gauge dataset
DOI	10.1109/ACCESS.2019.2938106
收录类别	SCI
语种	英语
资助项目	National Science Foundation of China[61505178] ; Henan Provincial Key Science and Technology Research Project[162102210018]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000487832600013
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4681
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Yong
作者单位	1.Zhengzhou Univ, Sch Phys & Engn, Zhengzhou 450001, Henan, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100049, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Li, Lei,Li, Yong,Lian, Kechao,et al. PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration[J]. IEEE ACCESS,2019,7:123280-123288.
APA	Li, Lei,Li, Yong,Lian, Kechao,Bian, Xiaoyu,Yang, Kuan,&Tian, Yongzhi.(2019).PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration.IEEE ACCESS,7,123280-123288.
MLA	Li, Lei,et al."PGC-Net: A Light Weight Convolutional Sequence Network for Digital Pressure Gauge Calibration".IEEE ACCESS 7(2019):123280-123288.