The development and utilization of low-cost near infrared imagers for fire detection and diagnostics

• Main Author: Wang, Xiao
• Format: Dissertation
• Language: English
• Published: University of Sheffield 2021

This research explores the use of near infrared spectrum for video fire detection and combustion diagnostics. The near infared spectrum is somehow very under-explored in these fields. A low-cost silicon-based backside illuminated CMOS image sensor was modified to become a monochrome sensor exposing its full spectral sensitivity. The sensor was tested to show its improved spectral sensitivity. The multi-spectrum fire detection combines stereo cameras with NIR only and NIR-visible spectrum respectively for robust fire detection. Flame image properties in both conditions are extensively studied, whereby the NIR-only channel corresponds to ROI extraction and texture feature extraction; the NIRvisible channel give rise to a unique colour model for false positive classification rejection. Machine learning algorithms were employed for fire recognition. Practical considerations of designing a fire detection system have been discussed in terms of sensor selection, feature extraction as well as choice of classification algorithms. Dual-band stereo video fire detection not only showed great potential for robust fire detection, but also for vision-based automated firefighting. In combustion diagnostics, low-cost NIR sensors were used for the imaging of combustion products of fuel-lean premixed flames, which demonstrated its effectiveness in potential flame instability related diagnostics. Moreover, the thermalcapability of NIR camera sensor were applied in conjunction with visible and schlieren imaging, to study the mechanism of fire propagation on solid fuel combustion. Finally, a summary was made with additional suggestions and speculations on the subject as a whole.