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High-speed Railway Real-time Localization Auxiliary Method based on Deep Neural Network
Dongjie Chen1,2; Wensheng Zhang1,2; Yang Yang1
Conference Name13th International Conference of Computational Methods in Sciences and Engineering
Conference Date2017-4-15
Conference PlaceAthens,Greece
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
Corresponding AuthorWensheng Zhang
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of 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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