Comparing ML based Segmentation Models on Jet Fire Radiation Zone
- URL: http://arxiv.org/abs/2107.03461v1
- Date: Wed, 7 Jul 2021 19:52:52 GMT
- Title: Comparing ML based Segmentation Models on Jet Fire Radiation Zone
- Authors: Carmina P\'erez-Guerrero, Adriana Palacios, Gilberto Ochoa-Ruiz,
Christian Mata, Miguel Gonzalez-Mendoza, Luis Eduardo Falc\'on-Morales
- Abstract summary: characterization of 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.
A data set of propane jet fires is used to train and evaluate the different approaches.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 criteria. The Hausdorff Distance and
Adjsted Random Index were the metrics with the highest correlation and the best
results were obtained from the UNet architecture with a Weighted Cross-Entropy
Loss. These results can be used in future research to extract more geometric
information from the segmentation masks or could even be implemented on other
types of fire accidents.
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