Affiliation:
1. Signal Processing Group, Institute of Telecommunications and Multimedia Applications (iTEAM), Universitat Politècnica de València, Camino de Vera, S/N, 46022 Valencia, Spain
Abstract
This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires. This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations. The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system. The system performance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire), for different probability of false alarm (PFA). The necessity of including decision fusion is thereby demonstrated.
Subject
General Environmental Science,General Biochemistry, Genetics and Molecular Biology,General Medicine
Cited by
21 articles.
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1. An intelligent wildfire identification method based on weighted boxes fusion and convolutional block attention module;International Journal of Parallel, Emergent and Distributed Systems;2024-01-31
2. Multi Sensor Network System for Early Detection and Prediction of Forest Fires in Southeast Asia;2023 33rd International Telecommunication Networks and Applications Conference;2023-11-29
3. A Review on Forest Fire Detection and Monitoring Systems;2023 International Conference on Electrical Engineering and Advanced Technology (ICEEAT);2023-11-05
4. Wildfire Detection Using HALE Meteorology Vehicles;2023 4th IEEE Global Conference for Advancement in Technology (GCAT);2023-10-06
5. Deployment of a UAV-Based Fire Detection System;2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring);2023-06