Intelligent Detection of Warning Bells at Level Crossings through Deep Transfer Learning for Smarter Railway Maintenance
Creators
- 1. Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
- 2. School of Innovation, Design and Engineering, Mälardalen University, Eskilstuna, Sweden; and Department of Computer Science and Media Technology, Linnaeus University, Växjö, Sweden
- 3. Department of Engineering, University of Naples "Parthenope", Naples, Italy
- 4. Digital & Lumada Solutions, Hitachi Rail STS
Description
Abstract:
Level Crossings are among the most critical railway assets, concerning both the risk of accidents and their maintainability, due to intersections with promiscuous traffic and difficulties in remotely monitoring their health status. Failures can be originated from several factors, including malfunctions in the bar mechanisms and warning devices, such as light signals and bells. This paper focuses on the intelligent detection of anomalies in warning bells through non-intrusive acoustic monitoring by: (1) introducing a new concept for autonomous monitoring of level crossings; (2) generating and sharing a specific dataset collecting relevant audio signals from publicly available audio recordings; (3) implementing and evaluating a solution combining deep learning and transfer learning for warning bell detection. The results show a high accuracy in detecting anomalies and suggest viability of the approach in real-world applications, especially where network cameras with on-board microphones are installed for multi-purpose level crossing surveillance.
Fundings and Disclaimer:
This research has received funding from the Shift2Rail Joint Undertaking (JU) under grant agreement No 881782 RAILS (Roadmaps for Artificial Intelligence (A.I.) integration in the raiL Sector). The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Shift2Rail JU members other than the Union.
The information and views set out in this document are those of the author(s) and do not necessarily reflect the official opinion of Shift2Rail Joint Undertaking. The JU does not guarantee the accuracy of the data included in this document. Neither the JU nor any person acting on the JU’s behalf may be held responsible for the use which may be made of the information contained therein.
Publication Notes:
This Journal Article is available in Open Access at: https://www.sciencedirect.com/science/article/pii/S0952197623005894
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