Infrared Flame Detection System Using Multiple Neural Networks

Author

Huseynov, Javid J. ; Baliga, Shankar ; Widmer, Alan ; Boger, Zvi

Author_Institution

Univ. of California Irvine, Irvine

fYear

2007

fDate

12-17 Aug. 2007

Firstpage

608

Lastpage

612

Abstract

A model for an infrared (IR) flame detection system using multiple artificial neural networks (ANN) is presented. The present work offers significant improvements over our previous design (Huseynov et al., 2005). Feature extraction only in the relevant frequency band using joint time-frequency analysis yields an input to a series of conjugate-gradient (CG) method-based ANNs. Each ANN is trained to distinguish all hydrocarbon flames from a particular type of environmental nuisance and ambient noise. Signal saturation caused by the increased intensity of IR sources at closer distances is resolved by adjustable gain control.

Keywords

conjugate gradient methods; environmental factors; environmental science computing; feature extraction; fires; flames; neural nets; object detection; safety systems; signal processing; ambient noise; artificial neural network; conjugate gradient method; environmental nuisance; feature extraction; frequency band; hydrocarbon flames; infrared flame detection system; joint time-frequency analysis; signal saturation; Artificial neural networks; Character generation; Feature extraction; Fires; Hydrocarbons; Infrared detectors; Neural networks; Signal resolution; Time frequency analysis; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2007. IJCNN 2007. International Joint Conference on

Conference_Location

Orlando, FL

ISSN

1098-7576

Print_ISBN

978-1-4244-1379-9

Electronic_ISBN

1098-7576

Type

conf

DOI

10.1109/IJCNN.2007.4371026

Filename

4371026