Machine Learning based Classification for Fire and Smoke Images Recognition*

Fires have become a more serious hazard to people's lives, property, and environment. Compared with the traditional techniques of fire detection, image technologies play a very promising role to overcome the problem of high false alarm rate. However, a major issue with these methods is their fastidious and long-time generation. In fact, the implemented algorithms are often produced using multi-feature technique, including chromatic characteristics, dynamic features, texture features and contour features. Therefore, we provide, in this paper, a study of some supervised machine learning algorithm for fire and smoke images recognition, and we compare it to a proposed model based on convolution neural network (CNN) algorithm. To do this, we consider a proper database composed by a total of 28334 images classified into three categories: 7329 fire images, 9205 smoke images and 11800 other images.