Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)
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Date
2020-02-01
Journal Title
Journal ISSN
Volume Title
Publisher
Hindawi Journal of Engineering
Abstract
)is paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification
system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most
consumer-grade fire detection systems relied solely on smoke detectors. )ese offer limited protection due to the type of fire
present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural
network (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep
learning due to their ability to perform feature extraction and classification in the same architecture. )e system is designed to
enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as
flames, smoke, and heat. )e incorporation of the convolutional neural networks enables broader coverage of the area of interest,
using visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in
real-time with location information. )e efficiency of the fire detection and notification system employed by standard fire
detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with
timely fire detection, alerting, and response time for firefighting. )e final experimental and performance evaluation results
showed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.
Description
Research Article
Keywords
development, multisensor, Convolutional Neural Networks, surveillance cameras