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Comparing Machine Learning based Segmentation Models on Jet Fire Radiation Zones by Pérez-Guerrero, Carmina; Palacios, Adriana; Ochoa-Ruiz, Gilberto; Mata, Christian; Gonzalez-Mendoza, Miguel; Falcón-Morales, Luis Eduardo is a scholarly article available to read on EtoBox.

What is Comparing Machine Learning based Segmentation Models on Jet Fire Radiation Zones about?

Risk assessment is relevant in any workplace, however there is a degree of unpredictability when dealing with flammable or hazardous materials so that detection of fire accidents by itself may not be enough. An example of this is the impingement of jet fires, where the heat fluxes of the flame could reach nearby equipment and dramatically increase the probability of a domino effect with catastrophic results. Because of this, the characterization of such fire accidents is important from a risk management point of view. One such characterization would be the segmentation of different radiation zones within the flame, so this paper presents an exploratory research regarding several traditional computer vision and Deep Learning segmentation approaches to solve this specific problem. A data set of propane jet fires is used to train and evaluate the different approaches and given the difference in the distribution of the zones and background of the images, different loss functions, that seek to alleviate data imbalance, are also explored. Additionally, different metrics are correlated to a manual ranking performed by experts to make an evaluation that closely resembles the expert's crite

Author

Pérez-Guerrero, Carmina; Palacios, Adriana; Ochoa-Ruiz, Gilberto; Mata, Christian; Gonzalez-Mendoza, Miguel; Falcón-Morales, Luis Eduardo

Published

2021

Language

EN