|High-speed Railway Real-time Localization Auxiliary Method based on Deep Neural Network|
|Dongjie Chen1,2; Wensheng Zhang1,2; Yang Yang1|
|会议名称||13th International Conference of Computational Methods in Sciences and Engineering|
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.
|作者单位||1.Institute of Automation, Chinese Academy of Sciences|
2.University of Chinese Academy of Sciences
|Dongjie Chen,Wensheng Zhang,Yang Yang. High-speed Railway Real-time Localization Auxiliary Method based on Deep Neural Network[C],2017.|
|ICCMSE_2017_29_paper（466KB）||会议论文||开放获取||CC BY-NC-SA||浏览 下载|