  Most of the automatic fire alarm systems detect the fire presence through
sensors like thermal, smoke, or flame. One of the new approaches to the problem
is the use of images to perform the detection. The image approach is promising
since it does not need specific sensors and can be easily embedded in different
devices. However, besides the high performance, the computational cost of the
used deep learning methods is a challenge to their deployment in portable
devices. In this work, we propose a new deep learning architecture that
requires fewer floating-point operations (flops) for fire recognition.
Additionally, we propose a portable approach for fire recognition and the use
of modern techniques such as inverted residual block, convolutions like
depth-wise, and octave, to reduce the model's computational cost. The
experiments show that our model keeps high accuracy while substantially
reducing the number of parameters and flops. One of our models presents 71\%
fewer parameters than FireNet, while still presenting competitive accuracy and
AUROC performance. The proposed methods are evaluated on FireNet and FiSmo
datasets. The obtained results are promising for the implementation of the
model in a mobile device, considering the reduced number of flops and
parameters acquired.
