@article{ref1,
title="Object detection at level crossing using deep learning",
journal="Micromachines (Basel)",
year="2020",
author="Fayyaz, Muhammad Asad Bilal and Johnson, Christopher",
volume="11",
number="12",
pages="e1005-e1005",
abstract="Multiple projects within the rail industry across different regions have been  initiated to address the issue of over-population. These expansion plans and upgrade  of technologies increases the number of intersections, junctions, and level  crossings. A level crossing is where a railway line is crossed by a road or right of  way on the level without the use of a tunnel or bridge. Level crossings still pose a  significant risk to the public, which often leads to serious accidents between rail,  road, and footpath users and the risk is dependent on their unpredictable behavior. For Great Britain, there were three fatalities and 385 near misses at level  crossings in 2015-2016. Furthermore, in its annual safety report, the Rail Safety  and Standards Board (RSSB) highlighted the risk of incidents at level crossings  during 2016/17 with a further six fatalities at level crossings including four  pedestrians and two road vehicles. The relevant authorities have suggested an  upgrade of the existing sensing system and the integration of new novel technology  at level crossings. The present work addresses this key issue and discusses the  current sensing systems along with the relevant algorithms used for post-processing  the information. The given information is adequate for a manual operator to make a  decision or start an automated operational cycle. Traditional sensors have certain  limitations and are often installed as a &quot;single sensor&quot;. The single sensor does not  provide sufficient information; hence another sensor is required. The algorithms  integrated with these sensing systems rely on the traditional approach, where  background pixels are compared with new pixels. Such an approach is not effective in  a dynamic and complex environment. The proposed model integrates deep learning  technology with the current Vision system (e.g., CCTV to detect and localize an  object at a level crossing). The proposed sensing system should be able to detect  and localize particular objects (e.g., pedestrians, bicycles, and vehicles at level  crossing areas.) The radar system is also discussed for a &quot;two out of two&quot; logic  interlocking system in case of fail-mechanism. Different techniques to train a deep  learning model are discussed along with their respective results. The model achieved  an accuracy of about 88% from the MobileNet model for classification and a loss  metric of 0.092 for object detection. Some related future work is also discussed.<p /> <p>Language: en</p>",
language="en",
issn="2072-666X",
doi="10.3390/mi11121055",
url="http://dx.doi.org/10.3390/mi11121055"
}