Annotation Pipeline for Railway Track Segmentation
Authors: Itziar Sagastiberri Fernández Maider Larrazabal Aurrekoetxea Daniel Ochoa de Eribe
Date: 29.08.2024
Abstract
As ADAS (Advanced Driver Assistance Systems) are becoming more common in the railway environment, the need for data to be able to develop those systems keeps increasing. We are reaching higher Grades of automation (GoA) in trains and subways, but there are not that many publicly available datasets to train the models that help us reach that autonomy; specially when compared to the number of datasets with automotive data. In this paper, we propose a pipeline for annotating rail tracks by using ASAM's OpenLABEL format and its labelling tool WebLabel. With the proposed pipeline we have demonstrated WebLabel's fitness for track annotation and we hope it will help in the release of future datasets for the railway environment. The pipeline helps reduce time in annotation in all the proposed steps. we see a significant change specially by projecting annotations between cameras in sequences that were recorded with more than one camera, where the total time of annotation was reduced by 43.41 %. We also propose a semiautomatic annotation step, where we segment the tracks and load the preannotations into WebLabel before the manual annotation. By doing this we managed to reduce the time spent annotating by 8.25%.
BIB_text
title = {Annotation Pipeline for Railway Track Segmentation},
keywds = {
annotation pipeline; multi-camera projection; semiauto-matic annotation; track segmentation
}
abstract = {
As ADAS (Advanced Driver Assistance Systems) are becoming more common in the railway environment, the need for data to be able to develop those systems keeps increasing. We are reaching higher Grades of automation (GoA) in trains and subways, but there are not that many publicly available datasets to train the models that help us reach that autonomy; specially when compared to the number of datasets with automotive data. In this paper, we propose a pipeline for annotating rail tracks by using ASAM's OpenLABEL format and its labelling tool WebLabel. With the proposed pipeline we have demonstrated WebLabel's fitness for track annotation and we hope it will help in the release of future datasets for the railway environment. The pipeline helps reduce time in annotation in all the proposed steps. we see a significant change specially by projecting annotations between cameras in sequences that were recorded with more than one camera, where the total time of annotation was reduced by 43.41 %. We also propose a semiautomatic annotation step, where we segment the tracks and load the preannotations into WebLabel before the manual annotation. By doing this we managed to reduce the time spent annotating by 8.25%.
}
isbn = {979-835035098-2},
date = {2024-08-29},
}
