  Fires have destructive power when they break out and affect their
surroundings on a devastatingly large scale. The best way to minimize their
damage is to detect the fire as quickly as possible before it has a chance to
grow. Accordingly, this work looks into the potential of AI to detect and
recognize fires and reduce detection time using object detection on an image
stream. Object detection has made giant leaps in speed and accuracy over the
last six years, making real-time detection feasible. To our end, we collected
and labeled appropriate data from several public sources, which have been used
to train and evaluate several models based on the popular YOLOv4 object
detector. Our focus, driven by a collaborating industrial partner, is to
implement our system in an industrial warehouse setting, which is characterized
by high ceilings. A drawback of traditional smoke detectors in this setup is
that the smoke has to rise to a sufficient height. The AI models brought
forward in this research managed to outperform these detectors by a significant
amount of time, providing precious anticipation that could help to minimize the
effects of fires further.
