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

doi:10.1016/j.neucom.2020.07.086

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	High-speed rail pole number recognition through deep representation and temporal redundancy
	Yang, Yang1 ; Zhang, Wensheng1 ; He, Zewen2 ; Li, Ding2
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2020-11-20
卷号	415 页码:201-214
通讯作者	Yang, Yang(yang.yang@ia.ac.cn)
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.
关键词	Region-based convolutional neural network Context information Object detection Number recognition Pole number High-speed rail
DOI	10.1016/j.neucom.2020.07.086
关键词[WOS]	LICENSE PLATE RECOGNITION ; EXTRACTION ; SEGMENTATION ; LOCALIZATION ; NETWORKS
收录类别	SCI
语种	英语
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]
项目资助者	National Key Research and Development Program of China ; Natural Science Foundation of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000579808700020
出版者	ELSEVIER
七大方向——子方向分类	目标检测、跟踪与识别
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42111
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
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
第一作者单位	精密感知与控制研究中心
通讯作者单位	精密感知与控制研究中心
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.

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