Low-complexity High-Performance Smoke/Fire Detection System in Smart City Environments Using Cross-Attention, Capsule based Optimized Siamese Convolutional Stacked Recurrent Neural Network

Author:

Ba Geri Berk A.B.1ORCID,Rabea Obad Abdullah Yousef1ORCID,Wang Liang1ORCID

Affiliation:

1. College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, China

Publisher

ACM

Reference20 articles.

1. E. Mahdipour, & C. Dadkhah, Automatic fire detection based on soft computing techniques: a review from 2000 to 2010, Artificial intelligence review, 42, 2014, 895-934.

2. K. Dimitropoulos, P. Barmpoutis, & N. Grammalidis, Spatio-temporal flame modeling and dynamic texture analysis for automatic video-based fire detection, IEEE transactions on circuits and systems for video technology, 25, 2014,, 339-351.

3. An Attention Enhanced Bidirectional LSTM for Early Forest Fire Smoke Recognition

4. Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube

5. Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks

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