Deep Learning for Detection of Railway Signs and Signals
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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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. ed. / Kohei Arai; Supriya Kapoor. Springer, 2020. p. 1-15 (Advances in Intelligent Systems and Computing, Vol. 943).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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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.
U2 - 10.1007/978-3-030-17795-9_1
DO - 10.1007/978-3-030-17795-9_1
M3 - Article in proceedings
SN - 978-3-030-17064-6
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