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

TL;DR

This paper introduces a unified approach for simultaneous smoke and fire detection using an evolutionary framework that combines self-supervised data augmentation techniques to improve model generalization across complex backgrounds.

Contribution

It proposes a novel evolutionary framework integrating self-supervised progressive data augmentation for concurrent smoke and fire detection, addressing shape uncertainty and background complexity.

Findings

Enhanced detection accuracy for smoke and fire

Improved model generalization on complex backgrounds

Effective data augmentation strategies demonstrated

Abstract

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.