  In this work we explore different Convolutional Neural Network (CNN)
architectures and their variants for non-temporal binary fire detection and
localization in video or still imagery. We consider the performance of
experimentally defined, reduced complexity deep CNN architectures for this task
and evaluate the effects of different optimization and normalization techniques
applied to different CNN architectures (spanning the Inception, ResNet and
EfficientNet architectural concepts). Contrary to contemporary trends in the
field, our work illustrates a maximum overall accuracy of 0.96 for full frame
binary fire detection and 0.94 for superpixel localization using an
experimentally defined reduced CNN architecture based on the concept of
InceptionV4. We notably achieve a lower false positive rate of 0.06 compared to
prior work in the field presenting an efficient, robust and real-time solution
for fire region detection.
