CASIA OpenIR  > 精密感知与控制研究中心  > 人工智能与机器学习
High-speed Railway Real-time Localization Auxiliary Method based on Deep Neural Network
Dongjie Chen1,2; Wensheng Zhang1,2; Yang Yang1
2017-04
Conference Name13th International Conference of Computational Methods in Sciences and Engineering
Conference Date2017-4-15
Conference PlaceAthens,Greece
Abstract
High-speed railway intelligent monitoring and management system is composed of schedule integration, geographic information, location services, and data mining technology for integration of time and space data. Assistant localization is a significant submodule of the intelligent monitoring system. In practical application, the general access is to capture the image sequences of the components by using a high-definition camera, digital image processing technique and target detection, tracking and even behavior analysis method. In this paper, we present an end-to-end character recognition method based on a deep CNN network called YOLO-toc for high-speed railway pillar plate number. Different from other deep CNNs, YOLO-toc is an end-to-end multi-target detection framework, furthermore, it exhibits a state-of-art performance on real-time detection with a nearly 50fps achieved on GPU (GTX960). Finally, we realize a real-time but high-accuracy pillar plate number recognition system and integrate natural scene OCR into a dedicated classification YOLO-toc model.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14583
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding AuthorWensheng Zhang
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Dongjie Chen,Wensheng Zhang,Yang Yang. High-speed Railway Real-time Localization Auxiliary Method based on Deep Neural Network[C],2017.
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