Deep Learning for Detection of Railway Signs and Signals

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Deep Learning for Detection of Railway Signs and Signals. / Karagiannis, Georgios; Olsen, Søren Ingvor; Pedersen, Kim Steenstrup.

Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference, CVC 2019, Las Vegas, Nevada, USA, 25-26 April 2019. red. / Kohei Arai; Supriya Kapoor. Bind 1 Springer, 2020. s. 1-15 (Advances in Intelligent Systems and Computing, Bind 943).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Karagiannis, G, Olsen, SI & Pedersen, KS 2020, Deep Learning for Detection of Railway Signs and Signals. i K Arai & S Kapoor (red), Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference, CVC 2019, Las Vegas, Nevada, USA, 25-26 April 2019. bind 1, Springer, Advances in Intelligent Systems and Computing, bind 943, s. 1-15, 2019 Computer Vision Conference, Las Vegas, USA, 25/04/2019.

APA

Karagiannis, G., Olsen, S. I., & Pedersen, K. S. (2020). Deep Learning for Detection of Railway Signs and Signals. I K. Arai, & S. Kapoor (red.), Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference, CVC 2019, Las Vegas, Nevada, USA, 25-26 April 2019 (Bind 1, s. 1-15). Springer. Advances in Intelligent Systems and Computing, Bind. 943

Vancouver

Karagiannis G, Olsen SI, Pedersen KS. Deep Learning for Detection of Railway Signs and Signals. I Arai K, Kapoor S, red., Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference, CVC 2019, Las Vegas, Nevada, USA, 25-26 April 2019. Bind 1. Springer. 2020. s. 1-15. (Advances in Intelligent Systems and Computing, Bind 943).

Author

Karagiannis, Georgios ; Olsen, Søren Ingvor ; Pedersen, Kim Steenstrup. / Deep Learning for Detection of Railway Signs and Signals. Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference, CVC 2019, Las Vegas, Nevada, USA, 25-26 April 2019. red. / Kohei Arai ; Supriya Kapoor. Bind 1 Springer, 2020. s. 1-15 (Advances in Intelligent Systems and Computing, Bind 943).

Bibtex

@inproceedings{652fa006f12540b796379c032f4425b0,
title = "Deep Learning for Detection of Railway Signs and Signals",
abstract = "Major railway lines need advance management systems based on accurate maps of their infrastructure. Asset detection is an important tool towards automation of processes and improved decision support on such systems. Due to lack of available data, limited research exists investigating railway asset detection, despite the rise of Artificial Neural Networks and the numerous investigations on autonomous driving. Here, we present a novel dataset used in real world projects for mapping railway assets. Also, we implement Faster R-CNN, a state of the art deep learning object detection method, for detection of signs and signals on this dataset. We achieved 79.36{\%} on detection and a 70.9{\%} mAP. The results were compromised by the small size of the objects, the low resolution of the images and the high similarity across classes.",
author = "Georgios Karagiannis and Olsen, {S{\o}ren Ingvor} and Pedersen, {Kim Steenstrup}",
year = "2020",
language = "English",
isbn = "978-3-030-17064-6",
volume = "1",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "1--15",
editor = "Arai, {Kohei } and Kapoor, {Supriya }",
booktitle = "Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference, CVC 2019, Las Vegas, Nevada, USA, 25-26 April 2019",
note = "2019 Computer Vision Conference, cvc 2019 ; Conference date: 25-04-2019 Through 26-04-2019",

}

RIS

TY - GEN

T1 - Deep Learning for Detection of Railway Signs and Signals

AU - Karagiannis, Georgios

AU - Olsen, Søren Ingvor

AU - Pedersen, Kim Steenstrup

PY - 2020

Y1 - 2020

N2 - Major railway lines need advance management systems based on accurate maps of their infrastructure. Asset detection is an important tool towards automation of processes and improved decision support on such systems. Due to lack of available data, limited research exists investigating railway asset detection, despite the rise of Artificial Neural Networks and the numerous investigations on autonomous driving. Here, we present a novel dataset used in real world projects for mapping railway assets. Also, we implement Faster R-CNN, a state of the art deep learning object detection method, for detection of signs and signals on this dataset. We achieved 79.36% on detection and a 70.9% mAP. The results were compromised by the small size of the objects, the low resolution of the images and the high similarity across classes.

AB - Major railway lines need advance management systems based on accurate maps of their infrastructure. Asset detection is an important tool towards automation of processes and improved decision support on such systems. Due to lack of available data, limited research exists investigating railway asset detection, despite the rise of Artificial Neural Networks and the numerous investigations on autonomous driving. Here, we present a novel dataset used in real world projects for mapping railway assets. Also, we implement Faster R-CNN, a state of the art deep learning object detection method, for detection of signs and signals on this dataset. We achieved 79.36% on detection and a 70.9% mAP. The results were compromised by the small size of the objects, the low resolution of the images and the high similarity across classes.

M3 - Article in proceedings

SN - 978-3-030-17064-6

VL - 1

T3 - Advances in Intelligent Systems and Computing

SP - 1

EP - 15

BT - Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference, CVC 2019, Las Vegas, Nevada, USA, 25-26 April 2019

A2 - Arai, Kohei

A2 - Kapoor, Supriya

PB - Springer

T2 - 2019 Computer Vision Conference

Y2 - 25 April 2019 through 26 April 2019

ER -

ID: 234446999