High-speed Railway Real-time Localization Auxiliary Method based on Deep Neural Network | |
Dongjie Chen1,2; Wensheng Zhang1,2; Yang Yang1 | |
2017-04 | |
会议日期 | 2017-4-15 |
会议地点 | Athens,Greece |
英文摘要 |
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. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/14583] |
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 | Wensheng Zhang |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Dongjie Chen,Wensheng Zhang,Yang Yang. High-speed Railway Real-time Localization Auxiliary Method based on Deep Neural Network[C]. 见:. Athens,Greece. 2017-4-15. |
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