Wildfire Smoke Detection Using Computational Intelligence Techniques Enhanced With Synthetic Smoke Plume Generation

被引：47

作者：

Labati, Ruggero Donida ^{[1
]}

Genovese, Angelo ^{[1
]}

Piuri, Vincenzo ^{[1
]}

Scotti, Fabio ^{[1
]}

机构：

[1] Univ Milan, Dept Comp Sci, I-26013 Crema Cr, Italy

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2013年 / 43卷 / 04期

关键词：

Computer vision; lattice Boltzmann; neural networks; simulation; smoke detection; virtual environment; wildfire; LATTICE-BOLTZMANN METHOD; AUTOMATA;

D O I：

10.1109/TSMCA.2012.2224335

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An early wildfire detection is essential in order to assess an effective response to emergencies and damages. In this paper, we propose a low-cost approach based on image processing and computational intelligence techniques, capable to adapt and identify wildfire smoke from heterogeneous sequences taken from a long distance. Since the collection of frame sequences can be difficult and expensive, we propose a virtual environment, based on a cellular model, for the computation of synthetic wildfire smoke sequences. The proposed detection method is tested on both real and simulated frame sequences. The results show that the proposed approach obtains accurate results.

引用

页码：1003 / 1012

页数：10