# Computer Vision-based Characterization of Large-scale Jet Flames using a Synthetic Infrared Image Generation Approach

arXiv: https://arxiv.org/abs/2206.02110 · HTML: https://mlsanity.com/paper/2206.02110/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 5 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1606.02147/) (2016)
- [Deep Learning vs. Traditional Computer Vision](https://mlsanity.com/paper/1910.13796/) (2019)
- [IMAGINE: Image Synthesis by Image-Guided Model Inversion](https://mlsanity.com/paper/2104.05895/) (2021)
- [Conditional Infilling GANs for Data Augmentation in Mammogram Classification](https://mlsanity.com/paper/1807.08093/) (2018)
- [FIRe-GAN: A novel Deep Learning-based infrared-visible fusion method for wildfire imagery](https://mlsanity.com/paper/2101.11745/) (2021)
- [Ambiguity of Objective Image Quality Metrics: A New Methodology for Performance Evaluation](https://mlsanity.com/paper/2101.07439/) (2021)
- [Comparing Machine Learning based Segmentation Models on Jet Fire Radiation Zones](https://mlsanity.com/paper/2107.03461/) (2021)
- [Experimental Large-Scale Jet Flames' Geometrical Features Extraction for Risk Management Using Infrared Images and Deep Learning Segmentation Methods](https://mlsanity.com/paper/2201.07931/) (2022)

## Abstract (arXiv)

Among the different kinds of fire accidents that can occur during industrial activities that involve hazardous materials, jet fires are one of the lesser-known types. This is because they are often involved in a process that generates a sequence of other accidents of greater magnitude, known as domino effect. Flame impingement usually causes domino effects, and jet fires present specific features that can significantly increase the probability of this happening. These features become relevant from a risk analysis perspective, making their proper characterization a crucial task. Deep Learning approaches have become extensively used for tasks such as jet fire characterization; however, these methods are heavily dependent on the amount of data and the quality of the labels. Data acquisition of jet fires involve expensive experiments, especially so if infrared imagery is used. Therefore, this paper proposes the use of Generative Adversarial Networks to produce plausible infrared images from visible ones, making experiments less expensive and allowing for other potential applications. The results suggest that it is possible to realistically replicate the results for experiments carried out using both visible and infrared cameras. The obtained results are compared with some previous experiments, and it is shown that similar results were obtained.