Deep Learning for Detection of Railway Signs and Signals

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.
Original languageEnglish
Title of host publicationAdvances in Computer Vision - Proceedings of the 2019 Computer Vision Conference, CVC 2019, Las Vegas, Nevada, USA, 25-26 April 2019
EditorsKohei Arai, Supriya Kapoor
Publication date2020
ISBN (Print)978-3-030-17064-6
Publication statusPublished - 2020
Event2019 Computer Vision Conference - Las Vegas, United States
Duration: 25 Apr 201926 Apr 2019


Conference2019 Computer Vision Conference
LandUnited States
ByLas Vegas
SeriesAdvances in Intelligent Systems and Computing

ID: 234446999