Open Access

ARTICLE

Convolutional Neural Network Model for Fire Detection in Real-Time Environment

Abdul Rehman, Dongsun Kim^*, Anand Paul

School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea

* Corresponding Author: Dongsun Kim. Email:

(This article belongs to the Special Issue: Recent Advances in Internet of Things and Emerging Technologies)

Computers, Materials & Continua 2023, 77(2), 2289-2307. https://doi.org/10.32604/cmc.2023.036435

Received 30 September 2022; Accepted 15 June 2023; Issue published 29 November 2023

Abstract

Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco. The authors have proposed an early fire detection system uses machine and/or deep learning algorithms. The article presents an Intelligent Industrial Monitoring System (IIMS) and introduces an Industrial Smart Social Agent (ISSA) in the Industrial SIoT (ISIoT) paradigm. The proffered ISSA empowers smart surveillance objects to communicate autonomously with other devices. Every Industrial IoT (IIoT) entity gets authorization from the ISSA to interact and work together to improve surveillance in any industrial context. The ISSA uses machine and deep learning algorithms for fire-related incident detection in the industrial environment. The authors have modeled a Convolutional Neural Network (CNN) and compared it with the four existing models named, FireNet, Deep FireNet, Deep FireNet V2, and Efficient Net for identifying the fire. To train our model, we used fire images and smoke sensor datasets. The image dataset contains fire, smoke, and no fire images. For evaluation, the proposed and existing models have been tested on the same. According to the comparative analysis, our CNN model outperforms other state-of-the-art models significantly.

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Keywords

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