Open AccessAUTOMATED FIRE DETECTION AND SURVEILLANCE SYSTEM
Author(s) -
K Ramya Laxmi,
Alwa Sreeja,
E Revanth,
Manasa Gourishetty,
Madasu Rushikesh
Publication year - 2022
Publication title -
ymer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.103
H-Index - 5
ISSN - 0044-0477
DOI - 10.37896/ymer21.04/41
Subject(s) - fire detection , computer science , convolutional neural network , limiting , artificial intelligence , real time computing , engineering , architectural engineering , mechanical engineering
The major goal of this project is to build a fire detection and surveillance system that is automatic. During surveillance, Convolutional Neural Networks will be employed to detect the fire (CNNs). Such methods, on the other hand, typically need greater processing time and memory, limiting their use in surveillance networks. We propose a low-cost fire detection CNN architecture for surveillance films in this research. This is mostly concerned with computational complexity and detection precision. The model is fine-tuned to balance efficiency and accuracy, taking into account the nature of the target problem and fire data. This system takes an image or video file as input and detects fire and fire percentages that are precise enough to prevent fire mishaps and save human lives.