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Few researches have studied simultaneous detection of smoke and flameaccompanying fires due to their different physical natures that lead touncertain fluid patterns. In this study, we collect a large image data set tore-label them as a multi-label image classification problem so as to identifysmoke and flame simultaneously. In order to solve the generalization ability ofthe detection model on account of the movable fluid objects with uncertainshapes like fire and smoke, and their not compactible natures as well as thecomplex backgrounds with high variations, we propose a data augment method byrandom image stitch to deploy resizing, deforming, position variation, andbackground altering so as to enlarge the view of the learner. Moreover, wepropose a self-learning data augment method by using the class activation mapto extract the highly trustable region as new data source of positive examplesto further enhance the data augment. By the mutual reinforcement between thedata augment and the detection model that are performed iteratively, bothmodules make progress in an evolutionary manner. Experiments show that theproposed method can effectively improve the generalization performance of themodel for concurrent smoke and fire detection.

Unified smoke and fire detection in an evolutionary framework with self-supervised progressive data augment