Detecting Flashover in a Room Fire Based on the Sequence of Thermal Infrared Images Using Convolutional Neural Networks

Flashover phenomena accompanying rapid re propagation in a room occur when the hot smoke from a re accumulates in the room's upper part.This phenomenon presents one of the most frightening and challenging situations for reghters.A typical approach to mitigate and prevent the impact of ashover is to train reghters to monitor a few common indicators of re in pre-ashover time, such as moving dark smoke, high heat, and re rollover.In actual compartment re events, these pre-ashover indicators are hard to recognize.Furthermore, determination of exact ashover time is dicult by just observing re activities while there are other vital rescue duties to do by reghters.Hence, automatic detection and prediction of ashover in real time are of paramount importance to save lives and reduce the cost of damages.Flashover prediction is still an open area of research by re safety experts.Deep convolutional neural networks are currently dominating the area of computer vision, and these state-of-the-art deep learning models have been successfully used in various applications, including object detection, localization, and segmentation.Unlike previous studies that use RGB images, sensors, and gauges, we utilized the power of deep learning techniques to detect ashover from image sequences captured by thermal infrared (IR) cameras.Our experimental results indicate that not only our proposed approach can detect ashover in IR video data with high precision, but it can detect ashover a few frames before happening.Our technique is a promising approach that can be used in future for ashover prediction in real time.