Machine Learning based Classification for Fire and Smoke Images Recognition*

被引：0

作者：

Jabnouni, Hedi ^{[1
,2
]}

Arfaoui, Imen ^{[2
]}

Cherni, Mohamed Ali ^{[2
]}

Bouchouicha, Moez ^{[3
]}

Sayadi, Mounir ^{[2
]}

机构：

[1] Univ Sousse, Inst Super Informat & Tech Commun H Sousse, H Sousse 4011, Tunisia

[2] Univ Tunis, ENSIT, LR13 ES03 SIME, Tunis 1008, Tunisia

[3] Univ Toulon & Var, Aix Marseille Univ, LIS, CNRS, F-83041 Toulon, France

来源：

2022 8TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT'22) | 2022年

关键词：

Convolutional neural network; Fire and smoke images; Image classification; Image recognition; Machine learning; VIDEO FIRE;

D O I：

10.1109/CODIT55151.2022.9803928

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：425 / 430

页数：6