Abstract
Using liquefied natural gas (LNG) as an alternative to conventional marine fuels can contribute to reaching carbon neutrality. Handling LNG requires tight safety measures due to the risk of explosion and frostbite. The present paper introduces a live monitoring approach to detect safety violations (persons in dangerous areas) and LNG leakage. The system utilizes a dual-camera system with regular RGB, and thermal vision and a machine learning approach suitable for the naval environment and movements like roll, pitch, and yaw. In lab tests, we showed a reliable detection of gas leakage and improved person detection.
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Acknowledgements
The authors would like to thank the German Federal Ministry of Economic Affairs and Energy (BMWi) for their support within the project “LNG Transfer—LNG Safety” (grant number 16KN062731).
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Jathe, N., Stern, H., Freitag, M. (2023). Development of a Hybrid Safety System Based on a Machine Learning Approach Using Thermal and RGB Data. In: Theilliol, D., Korbicz, J., Kacprzyk, J. (eds) Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis. ACD 2022. Studies in Systems, Decision and Control, vol 467. Springer, Cham. https://doi.org/10.1007/978-3-031-27540-1_24
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