  Few researches have studied simultaneous detection of smoke and flame
accompanying fires due to their different physical natures that lead to
uncertain fluid patterns. In this study, we collect a large image data set to
re-label them as a multi-label image classification problem so as to identify
smoke and flame simultaneously. In order to solve the generalization ability of
the detection model on account of the movable fluid objects with uncertain
shapes like fire and smoke, and their not compactible natures as well as the
complex backgrounds with high variations, we propose a data augment method by
random image stitch to deploy resizing, deforming, position variation, and
background altering so as to enlarge the view of the learner. Moreover, we
propose a self-learning data augment method by using the class activation map
to extract the highly trustable region as new data source of positive examples
to further enhance the data augment. By the mutual reinforcement between the
data augment and the detection model that are performed iteratively, both
modules make progress in an evolutionary manner. Experiments show that the
proposed method can effectively improve the generalization performance of the
model for concurrent smoke and fire detection.
