RailNet: An Information Aggregation Network for Rail Track Segmentation

Haoran Li; Qichao Zhang; Dongbin Zhao; Yaran Chen

doi:10.1109/IJCNN48605.2020.9206967

RailNet: An Information Aggregation Network for Rail Track Segmentation

Haoran Li
, Qichao Zhang
, Dongbin Zhao
, Yaran Chen

University of Chinese Academy of Sciences

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

31 Citations (Scopus)

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 RailNet can achieve the best performance so far in the dataset of trains.

Original language	English
Title of host publication	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728169262
DOIs	https://doi.org/10.1109/IJCNN48605.2020.9206967
Publication status	Published - Jul 2020
Event	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/Territory	United Kingdom
City	Virtual, Glasgow
Period	19/07/20 → 24/07/20

Access to Document

10.1109/IJCNN48605.2020.9206967

Cite this

Li, H., Zhang, Q., Zhao, D., & Chen, Y. (2020). RailNet: An Information Aggregation Network for Rail Track Segmentation. In 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings Article 9206967 (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN48605.2020.9206967

@inproceedings{2ecdfd2fb012404caa1d7802019e0f3b,

title = "RailNet: An Information Aggregation Network for Rail Track Segmentation",

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 RailNet can achieve the best performance so far in the dataset of trains.",

author = "Haoran Li and Qichao Zhang and Dongbin Zhao and Yaran Chen",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Joint Conference on Neural Networks, IJCNN 2020 ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

month = jul,

doi = "10.1109/IJCNN48605.2020.9206967",

language = "English",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings",

}

Li, H, Zhang, Q, Zhao, D & Chen, Y 2020, RailNet: An Information Aggregation Network for Rail Track Segmentation. in 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings., 9206967, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., 2020 International Joint Conference on Neural Networks, IJCNN 2020, Virtual, Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/IJCNN48605.2020.9206967

RailNet: An Information Aggregation Network for Rail Track Segmentation. / Li, Haoran; Zhang, Qichao; Zhao, Dongbin et al.
2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9206967 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - RailNet

T2 - 2020 International Joint Conference on Neural Networks, IJCNN 2020

AU - Li, Haoran

AU - Zhang, Qichao

AU - Zhao, Dongbin

AU - Chen, Yaran

PY - 2020/7

Y1 - 2020/7

N2 - 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 RailNet can achieve the best performance so far in the dataset of trains.

AB - 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 RailNet can achieve the best performance so far in the dataset of trains.

UR - https://www.scopus.com/pages/publications/85093857308

U2 - 10.1109/IJCNN48605.2020.9206967

DO - 10.1109/IJCNN48605.2020.9206967

M3 - Conference Proceeding

AN - SCOPUS:85093857308

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 19 July 2020 through 24 July 2020

ER -

RailNet: An Information Aggregation Network for Rail Track Segmentation

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