Fire is considered one of the most serious threats to human lives which results in a high probability of fatalities. Those severe consequences stem from the heavy smoke emitted from a fire that mostly restricts the visibility of escaping victims and rescuing squad. In such hazardous circumstances, the use of a vision-based human detection system is able to improve the ability to save more lives. To this end, a thermal and infrared imaging fusion strategy based on multiple cameras for human detection in low-visibility scenarios caused by smoke is proposed in this paper. By processing with multiple cameras, vital information can be gathered to generate more useful features for human detection. Firstly, the cameras are calibrated using a Light Heating Chessboard. Afterward, the features extracted from the input images are merged prior to being passed through a lightweight deep neural network to perform the human detection task. The experiments conducted on an NVIDIA Jetson Nano computer demonstrated that the proposed method can process with reasonable speed and can achieve favorable performance with a [email protected] of 95%.

翻译：火灾被认为是对人类生命最严重的威胁之一，具有极高的致死概率。其严重后果源于火灾产生的大量浓烟，这些浓烟严重限制了逃生人员与救援队伍的视线。在如此危险的环境中，使用基于视觉的人体检测系统能够提升拯救更多生命的能力。为此，本文提出一种基于多摄像头的热红外与红外成像融合策略，用于烟雾导致的低能见度场景中的人体检测。通过多摄像头处理，可以收集关键信息以生成更有效的人体检测特征。首先，使用光热棋盘对摄像头进行标定。随后，在将输入图像传递至轻量级深度神经网络进行人体检测任务之前，对从图像中提取的特征进行融合。在NVIDIA Jetson Nano计算机上进行的实验表明，该方法能够以合理的速度处理，并实现了95%的[email protected]的优异性能。