CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 深度强化学习
RailNet: An Information aggregation network for rail track segmentation
Li, Haoran1,2; Zhang, Qichao1,2; Zhao, Dongbin1,2; Chen, Yaran1,2
2020-07
Conference NameInternational Joint Conference on Neural Networks (IJCNN)
Conference Date2020-7-19
Conference PlaceUK
Abstract

As the basis of scenes understanding for the track inspection task, track segmentation is challenging due to the various illumination conditions, track crossing, and plant coverage. Since the rail has a strong shape prior, strict rail spacing and special distribution in the image, making full use of the spatial information of the rail features becomes an important factor to improve the accuracy of rail segmentation. In this paper, an information aggregation module is proposed to enhance the spatial relationship between pixels of the rail features. In other words, this module expands the receptive field. Furthermore, we build an information aggregation network based on this module, which is called as RailNet. Finally, the RailNet is evaluated in an open train track dataset. Experimental results show that RailNetcan achieve the best performance so far in the dataset of trains.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40314
Collection复杂系统管理与控制国家重点实验室_深度强化学习
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Haoran,Zhang, Qichao,Zhao, Dongbin,et al. RailNet: An Information aggregation network for rail track segmentation[C],2020.
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