Real-time Human Detection in Fire Scenarios using Infrared and Thermal Imaging Fusion

TL;DR

This paper presents a real-time human detection system in fire scenarios using fused infrared and thermal images from multiple cameras, leveraging deep learning to improve rescue operations in smoke-filled environments.

Contribution

It introduces a novel multi-camera fusion strategy with a lightweight neural network for effective human detection in low-visibility fire conditions.

Findings

Achieved 95% mAP@0.5 performance.

Demonstrated real-time processing on NVIDIA Jetson Nano.

Enhanced detection accuracy in smoke-affected environments.

Abstract

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 mAP@0.5 of 95%.