Early fire detection using convolutional neural networks during surveillance for effective disaster management

• Published in: Neurocomputing (Amsterdam) Vol. 288; pp. 30 - 42
• Main Authors: Muhammad, Khan, Ahmad, Jamil, Baik, Sung Wook
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 02.05.2018
• Subjects: Deep learning; Disaster management; Fire detection; Image classification; Learning vision; Machine learning; Surveillance networks; Learning vision; Deep learning; Fire detection; Machine learning; Disaster management; Image classification; Surveillance networks
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2017.04.083

Fire disasters are man-made disasters, which cause ecological, social, and economic damage. To minimize these losses, early detection of fire and an autonomous response are important and helpful to disaster management systems. Therefore, in this article, we propose an early fire detection framework using fine-tuned convolutional neural networks for CCTV surveillance cameras, which can detect fire in varying indoor and outdoor environments. To ensure the autonomous response, we propose an adaptive prioritization mechanism for cameras in the surveillance system. Finally, we propose a dynamic channel selection algorithm for cameras based on cognitive radio networks, ensuring reliable data dissemination. Experimental results verify the higher accuracy of our fire detection scheme compared to state-of-the-art methods and validate the applicability of our framework for effective fire disaster management.