High-speed rail pole number recognition through deep representation and temporal redundancy
文献类型：期刊论文
| 作者 | Yang, Yang2 ; Zhang, Wensheng2; He, Zewen1; Li, Ding1
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2020-11-20 |
| 卷号 | 415页码:201-214 |
| ISSN号 | 0925-2312 |
| 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 |
| 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. |
| WOS关键词 | LICENSE PLATE RECOGNITION ; EXTRACTION ; SEGMENTATION ; LOCALIZATION ; NETWORKS |
| 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 |
| 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.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, 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. |
入库方式： OAI收割
来源：自动化研究所
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