High-speed rail pole number recognition through deep representation and temporal redundancy

doi:10.1016/j.neucom.2020.07.086

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	High-speed rail pole number recognition through deep representation and temporal redundancy
	Yang, Yang 1; Zhang, Wensheng 1; He, Zewen 2; Li, Ding 2
刊名	NEUROCOMPUTING
	2020-11-20
卷号	415 页码:201-214
关键词	Region-based convolutional neural network Context information Object detection Number recognition Pole number High-speed rail
ISSN号	0925-2312
DOI	10.1016/j.neucom.2020.07.086
通讯作者	Yang, Yang(yang.yang@ia.ac.cn)
英文摘要	Pole number recognition is highly important for the positioning tasks in high-speed rail catenary systems. The complicated working environment poses difficulties for number recognition algorithms, and this situation becomes even more challenging when illumination changes, image blurs and occlusions are considered. In this work, we present a high-accuracy pole number recognition framework including a high-performance cascaded CNN-based Detection and Recognition YOLO (DR-YOLO) and a temporal redundancy approach. First, the DR-YOLO utilizes global features for coarse plate detection and local features for accurate number recognition. Next, context-based combination of adjacent frames utilizes the complementarity and consistency of the same pole number recognition results and generates a unique result. The context-based amendment of adjacent poles utilizes the continuity of adjacent poles and corrects the fault or partial missing results caused by blur or occlusions. Extensive experimental testing is performed on 4 datasets representing high-speed train routine working environments. The reported experimental results validate the effectiveness and efficiency of the proposed approach. (C) 2020 Elsevier B.V. All rights reserved.
资助项目	National Key Research and Development Program of China[2016QY03D0500] ; Natural Science Foundation of China[61772525] ; Natural Science Foundation of China[61702517] ; Natural Science Foundation of China[61806202]
WOS关键词	LICENSE PLATE RECOGNITION ; EXTRACTION ; SEGMENTATION ; LOCALIZATION ; NETWORKS
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000579808700020
资助机构	National Key Research and Development Program of China ; Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42110]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Yang, Yang
作者单位	1.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Yang, Yang,Zhang, Wensheng,He, Zewen,et al. High-speed rail pole number recognition through deep representation and temporal redundancy[J]. NEUROCOMPUTING,2020,415:201-214.
APA	Yang, Yang,Zhang, Wensheng,He, Zewen,&Li, Ding.(2020).High-speed rail pole number recognition through deep representation and temporal redundancy.NEUROCOMPUTING,415,201-214.
MLA	Yang, Yang,et al."High-speed rail pole number recognition through deep representation and temporal redundancy".NEUROCOMPUTING 415(2020):201-214.