A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM

Meng Yang,Yuchao Liu,Yongming Bian,Xuying Fan

doi:10.3390/a11060079

Meng Yang, Yuchao Liu + Show 2 more

Open Access

https://doi.org/10.3390/a11060079

Copy DOI

Journal: Algorithms	Publication Date: May 25, 2018
Citations: 15	License type: CC BY 4.0

Affiliation: Tongji University

Abstract

Automatic fire detection, which can detect and raise the alarm for fire early, is expected to help reduce the loss of life and property as much as possible. Due to its advantages over traditional methods, image processing technology has been applied gradually in fire detection. In this paper, a novel algorithm is proposed to achieve fire image detection, combined with Tchebichef (sometimes referred to as Chebyshev) moment invariants (TMIs) and particle swarm optimization-support vector machine (PSO-SVM). According to the correlation between geometric moments and Tchebichef moments, the translation, rotation, and scaling (TRS) invariants of Tchebichef moments are obtained first. Then, the TMIs of candidate images are calculated to construct feature vectors. To gain the best detection performance, a PSO-SVM model is proposed, where the kernel parameter and penalty factor of support vector machine (SVM) are optimized by particle swarm optimization (PSO). Then, the PSO-SVM model is utilized to identify the fire images. Compared with algorithms based on Hu moment invariants (HMIs) and Zernike moment invariants (ZMIs), the experimental results show that the proposed algorithm can improve the detection accuracy, achieving the highest detection rate of 98.18%. Moreover, it still exhibits the best performance even if the size of the training sample set is small and the images are transformed by TRS.

Highlights

Fire has played a significant role in promoting the progress of human civilization
It continues to exhibit the best performance even if the size of the training sample set is small and the images are transformed by TRS, proving its effectiveness and feasibility
25 fire images and 20 non-fire images were randomly selected as the training sample set used to construct the particle swarm optimization-support vector machine (PSO-support vector machine (SVM)) model

Summary

Introduction

Fire has played a significant role in promoting the progress of human civilization. without proper management, it is one of the major disasters causing huge loss of human lives and property all over the world. In the past few years, Siemens launched a FDT221 fire detector It is equipped with two redundant heat sensors, which monitor rooms in which a temperature rise is expected in case of fire. Mircom launched the MIX-200 series of intelligent sensors for residential applications, equipped with photoelectric smoke detectors and electronic thermistors. These sensors may work efficiently in some particular cases, they suffer from large propagation delays of smoke and temperature, resulting in the increase of fire detection latency. Other algorithms, based on beam or aspirated smoke detectors, have been used to attempt to reduce the detection latency. They cannot solve the practical problem completely [2]

Methods

Results

Conclusion