Open AccessReal Time Fire detection and Localization in Video sequences using Deep Learning framework for Smart Building
Author(s) -
P. Sridhar,
R. R. Sathiya
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1916/1/012027
Subject(s) - computer science , fire detection , frame (networking) , frame rate , deep learning , artificial intelligence , work (physics) , false alarm , real time computing , alarm , object detection , architecture , constant false alarm rate , building automation , computer vision , frame work , simulation , pattern recognition (psychology) , architectural engineering , engineering , telecommunications , mechanical engineering , art , physics , visual arts , thermodynamics , aerospace engineering
This work presents autonomous electrical fire-detection and localization using computer vision based techniques. The proposed work uses YOLO v2 to extract the electrical fire features more effectively than other conventional and machine learning approaches. This working model is tested on commercial and residential building as well as indoor and outdoor environments. This framework has achieved high detection accuracy and low false alarm rate. Besides, the proposed frame work can be used for early real-time electrical fire detection in surveillance videos and we present experimental results for electrical fire localization in CCTV footage using the deep learning architecture proposed in this work.