Real-time long short-term glance-based fire detection using a CNN-LSTM neural network. (8th September 2021)

• Record Type: Journal Article
• Authors: Nguyen, Huan Van; Pham, Thang Xuan; Le, Cuong Nguyen
• Is Part Of: International journal of intelligent information and database systems. Volume 14:Number 4(2021)
• Page Start: 349
• Page End: 364
• Publication Date: 2021-09-08
• Subjects: fire detection -- long short-term memory -- LSTM -- temporal CNN; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

Vision-based fire detection is widely studied recently to reduce the damage of fire disaster thanks to the advantages of software-based methods comparing to traditional hardware-based fire detection using sensors. This paper presents a novel method for fire detection using the convolutional neural networks on image sequences of videos to extract both the spatial and temporal information for fire classification. The system includes a CNN network to extract the image features, and short-term and long-term stages at the end for classification. Experiments carried out on the common public datasets show promising results in terms of performance in comparison to the previous works.