基于条件生成对抗网络的快速火灾发展（闪络）预测
Predicting Rapid Fire Growth (Flashover) Using Conditional Generative Adversarial Networks
摘要：
当火焰由于高温而通过裂缝迅速蔓延时，就会发生闪络。对于那些经常遇到它们的人来说，闪火是最可怕和最具挑战性的火灾现象之一：消防员。消防员的安全和生命通常取决于他们在闪络发生之前预测闪络的能力。典型的闪前火灾特征包括黑烟、高热和翻滚（“天使指”），并可通过颜色、大小和形状进行量化。使用来自消防员身体摄像机的彩色视频流，我们应用生成对抗神经网络进行图像增强。训练神经网络以增强视频中非常暗的火灾和烟雾模式，并监测烟雾和火灾区域的动态变化。有限的闪络培训视频的初步测试表明，我们预测闪络早在55秒之前发生。
英文摘要：
A flashover occurs when a fire spreads very rapidly through crevices due to intense heat. Flashovers present one of the most frightening and challenging fire phenomena to those who regularly encounter them: firefighters. Firefighters' safety and lives often depend on their ability to predict flashovers before they occur. Typical pre-flashover fire characteristics include dark smoke, high heat, and rollover ("angel fingers") and can be quantified by color, size, and shape. Using a color video stream from a firefighter's body camera, we applied generative adversarial neural networks for image enhancement. The neural networks were trained to enhance very dark fire and smoke patterns in videos and monitor dynamic changes in smoke and fire areas. Preliminary tests with limited flashover training videos showed that we predicted a flashover as early as 55 seconds before it occurred.
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。
