Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection

Samarth, G.; Bhowmik, N.; Breckon, T.P.

Abstract

In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imagery. We consider the performance of experimentally defined, reduced complexity deep CNN architectures for this task and evaluate the effects of different optimization and normalization techniques applied to different CNN architectures (spanning the Inception, ResNet and EfficientNet architectural concepts). Contrary to contemporary trends in the field, our work illustrates a maximum overall accuracy of 0.96 for full frame binary fire detection and 0.94 for superpixel localization using an experimentally defined reduced CNN architecture based on the concept of InceptionV4. We notably achieve a lower false positive rate of 0.06 compared to prior work in the field presenting an efficient, robust and real-time solution for fire region detection.

Citation

Samarth, G., Bhowmik, N., & Breckon, T. (2019, December). Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection. Presented at 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019), Boca Raton, Florida, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019)
• Start Date: Dec 16, 2019
• End Date: Dec 19, 2019
• Acceptance Date: Sep 21, 2019
• Online Publication Date: Feb 17, 2020
• Publication Date: Dec 16, 2019
• Deposit Date: Dec 20, 2019
• Publicly Available Date: Sep 4, 2020
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 653-658
• Book Title: Proceedings of the 18th IEEE International Conference on Machine Learning and Applications ICMLA 2019
• DOI: https://doi.org/10.1109/icmla.2019.00119
• Public URL: https://durham-repository.worktribe.com/output/1141363
• Publisher URL: https:/doi.org/10.1109/ICMLA.2019.00119