Experimental Large-Scale Jet Flames' Geometrical Features Extraction for
Risk Management Using Infrared Images and Deep Learning Segmentation Methods
- URL: http://arxiv.org/abs/2201.07931v1
- Date: Thu, 20 Jan 2022 00:50:41 GMT
- Title: Experimental Large-Scale Jet Flames' Geometrical Features Extraction for
Risk Management Using Infrared Images and Deep Learning Segmentation Methods
- Authors: Carmina P\'erez-Guerrero, Adriana Palacios, Gilberto Ochoa-Ruiz,
Christian Mata, Joaquim Casal, Miguel Gonzalez-Mendoza, Luis Eduardo
Falc\'on-Morales
- Abstract summary: Jet fires are relatively small and have the least severe effects among the diverse fire accidents that can occur in industrial plants.
This research work explores the application of deep learning models in an alternative approach that uses the semantic segmentation of jet fires flames.
- Score: 0.9236074230806579
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Jet fires are relatively small and have the least severe effects among the
diverse fire accidents that can occur in industrial plants; however, they are
usually involved in a process known as the domino effect, that leads to more
severe events, such as explosions or the initiation of another fire, making the
analysis of such fires an important part of risk analysis. This research work
explores the application of deep learning models in an alternative approach
that uses the semantic segmentation of jet fires flames to extract main
geometrical attributes, relevant for fire risk assessments. A comparison is
made between traditional image processing methods and some state-of-the-art
deep learning models. It is found that the best approach is a deep learning
architecture known as UNet, along with its two improvements, Attention UNet and
UNet++. The models are then used to segment a group of vertical jet flames of
varying pipe outlet diameters to extract their main geometrical
characteristics. Attention UNet obtained the best general performance in the
approximation of both height and area of the flames, while also showing a
statistically significant difference between it and UNet++. UNet obtained the
best overall performance for the approximation of the lift-off distances;
however, there is not enough data to prove a statistically significant
difference between Attention UNet and UNet++. The only instance where UNet++
outperformed the other models, was while obtaining the lift-off distances of
the jet flames with 0.01275 m pipe outlet diameter. In general, the explored
models show good agreement between the experimental and predicted values for
relatively large turbulent propane jet flames, released in sonic and subsonic
regimes; thus, making these radiation zones segmentation models, a suitable
approach for different jet flame risk management scenarios.
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