  Automatic visual fire detection is used to complement traditional fire
detection sensor systems (smoke/heat). In this work, we investigate different
Convolutional Neural Network (CNN) architectures and their variants for the
non-temporal real-time bounds detection of fire pixel regions in video (or
still) imagery. Two reduced complexity compact CNN architectures
(NasNet-A-OnFire and ShuffleNetV2-OnFire) are proposed through experimental
analysis to optimise the computational efficiency for this task. The results
improve upon the current state-of-the-art solution for fire detection,
achieving an accuracy of 95% for full-frame binary classification and 97% for
superpixel localisation. We notably achieve a classification speed up by a
factor of 2.3x for binary classification and 1.3x for superpixel localisation,
with runtime of 40 fps and 18 fps respectively, outperforming prior work in the
field presenting an efficient, robust and real-time solution for fire region
detection. Subsequent implementation on low-powered devices (Nvidia Xavier-NX,
achieving 49 fps for full-frame classification via ShuffleNetV2-OnFire)
demonstrates our architectures are suitable for various real-world deployment
applications.
