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

TL;DR

This paper introduces a synthetic infrared image generation method using GANs to facilitate jet fire characterization, reducing experimental costs and enabling effective deep learning applications.

Contribution

The paper presents a novel GAN-based approach to generate realistic infrared images from visible images for jet fire analysis, addressing data scarcity and labeling challenges.

Findings

Generated infrared images closely match real infrared data

Comparable results to previous experiments demonstrate method validity

Reduces costs and data requirements for jet fire analysis

Abstract

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.