Automatic real-time fire distance, size and power measurement driven by stereo camera and deep learning

Automatic real-time fire characterization is a crucial requirement of future smart firefighting. This work proposes a novel computer vision method to automatically measure the fire heat release rate, even when the camera is moving in real-time. Firstly, a portable binocular stereo camera is used to capture the real-time fire video stream that is fed into a pre-trained computer-vision model frame-by-frame to detect the fire region. By identifying the fire location inside the image, the real-time distance between the camera and the fire source is determined. This fire distance helps re-scale the images to match the input scale of the AI-image Fire Calorimetry. Then, the deep learning model can automatically output the transient fire power in real time. Results show that the stereo vision system is capable of accurately measuring the distance between the camera and the fire source, flame height, and power, with a relative error of less than 20%. This work provides an automatic and flexible way to measure the distance, power and hazard of fire in real-time, and such a method has broad applications in firefighting operations and decision-making.

Automated summary

This study proposes a novel computer vision method to automatically measure the real-time fire heat release rate accurately, even when the camera is moving. A portable binocular stereo camera captures the real-time fire video stream, which is then fed into a pre-trained computer-vision model to detect the fire region. The distance between the camera and the fire source is determined by identifying the fire location, enabling the deep learning model to output the transient fire power in real time.