基于计算机视觉的大尺度射流火焰红外合成图像表征
Computer Vision-based Characterization of Large-scale Jet Flames using a Synthetic Infrared Image Generation Approach
摘要：
在涉及危险材料的工业活动中可能发生的不同类型的火灾事故中，喷射火灾是鲜为人知的类型之一。这是因为他们经常参与一个过程，产生一系列更大规模的其他事故，称为多米诺骨牌效应。火焰冲击通常会引起多米诺骨牌效应，而喷射火灾的特殊性会显著增加这种情况发生的可能性。从风险分析的角度来看，这些特征变得相关，使其正确表征成为一项关键任务。深度学习方法已被广泛用于喷射火焰表征等任务;然而，这些方法严重依赖于数据量和标签的质量。喷射火焰的数据采集涉及昂贵的实验，特别是如果使用红外成像。因此，本文提出了使用生成对抗网络从可见光图像中生成合理的红外图像，使实验成本更低，并允许其他潜在的应用。结果表明，有可能真实地复制使用可见光和红外相机进行的实验结果。所得结果与以前的一些实验进行了比较，结果表明，得到了类似的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
