Yang, P, Muhammad, K, Ahmad, J, Lv, Z, Bellavista, P and Baik, S (2018) Efficient Deep CNN-Based Fire Detection and Localisation in Video Surveillance Applications. IEEE Transactions on Systems Man and Cybernetics: Systems. ISSN 2168-2216

Preview

Text SMCA-17-10-1204-Final-3.pdf - Accepted Version Download (2MB) | Preview

Abstract

Convolutional neural networks (CNN) have yielded state-of-the-art performance in image classification and other computer vision tasks. Their application in fire detection systems will substantially improve detection accuracy, which will eventually minimize fire disasters and reduce the ecological and social ramifications. However, the major concern with CNN-based fire detection systems is their implementation in real-world surveillance networks, due to their high memory and computational requirements for inference. In this work, we propose an energy-friendly and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire. It uses smaller convolutional kernels and contains no dense, fully connected layers, which helps keep the computational requirements to a minimum. Despite its low computational needs, the experimental results demonstrate that our proposed solution achieves accuracies that are comparable to other, more complex models, mainly due to its increased depth. Moreover, the paper shows how a trade-off can be reached between fire detection accuracy and efficiency, by considering the specific characteristics of the problem of interest and the variety of fire data.

Item Type:	Article
Additional Information:	© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	Computer Science & Mathematics
Publisher:	Institute of Electrical and Electronics Engineers
Date Deposited:	03 Jul 2018 14:18
Last Modified:	04 Sep 2021 02:51
URI:	https://researchonline.ljmu.ac.uk/id/eprint/8341

View Item