Computer Vision-based Characterization of Large-scale Jet Flames using a Synthetic Infrared Image Generation Approach
This addresses the need for cost-effective data in industrial safety applications, though it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of expensive data acquisition for characterizing large-scale jet flames in industrial risk analysis by proposing a Generative Adversarial Network to generate synthetic infrared images from visible ones, showing that it realistically replicates results from experiments using both camera types.
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.