TY - GEN
T1 - Building a balanced and well-rounded dataset for railway asset detection
AU - Eickeler, Felix
AU - Borrmann, André
N1 - Publisher Copyright:
© 2021 Universitätsverlag der Technischen Universität Berlin. All Rights Reserved.
PY - 2021
Y1 - 2021
N2 - The entire railway network in Europe has a total length of close to 200 thousand kilometres and is one of the main components of European infrastructure (Eurostat Database 2021). Modernising and maintenance is a sizable effort, and due to the long lifespan of railway links, documentation is discontinued, incomplete, or lost. Using survey methods and recreating accurate as-is documentation improve the efficiency and effectivity of maintaining the rail network. In this paper, we present one major building block in creating such a recognition model. While focusing on images and semantic segmentation, the paper describes how a well-rounded dataset for training ML models can be constructed efficiently. Such a dataset is the missing part in adapting modern image recognition systems to railways and providing semantic information for a fully usable building information model (BIM).
AB - The entire railway network in Europe has a total length of close to 200 thousand kilometres and is one of the main components of European infrastructure (Eurostat Database 2021). Modernising and maintenance is a sizable effort, and due to the long lifespan of railway links, documentation is discontinued, incomplete, or lost. Using survey methods and recreating accurate as-is documentation improve the efficiency and effectivity of maintaining the rail network. In this paper, we present one major building block in creating such a recognition model. While focusing on images and semantic segmentation, the paper describes how a well-rounded dataset for training ML models can be constructed efficiently. Such a dataset is the missing part in adapting modern image recognition systems to railways and providing semantic information for a fully usable building information model (BIM).
UR - http://www.scopus.com/inward/record.url?scp=85134243104&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85134243104
T3 - EG-ICE 2021 Workshop on Intelligent Computing in Engineering, Proceedings
SP - 475
EP - 485
BT - EG-ICE 2021 Workshop on Intelligent Computing in Engineering, Proceedings
A2 - Abualdenien, Jimmy
A2 - Borrmann, Andre
A2 - Ungureanu, Lucian-Constantin
A2 - Hartmann, Timo
PB - Technische Universitat Berlin
T2 - 28th International Workshop on Intelligent Computing in Engineering of the European Group for Intelligent Computing in Engineering, EG-ICE 2021
Y2 - 30 June 2021 through 2 July 2021
ER -