Naslov
Comparative analysis of semantic segmentation and object detection methods for early fire detection

Autori
Arlovic, Matej ; Maric, Petar ; Balen, Josip ; Damjanovic, Davor ; Vdovjak, Kresimir

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
The Second Serbian International Conference on Applied Artificial Intelligence, Kragujevac - Book of Abstracts / - , 2023, 64-64

ISBN
978-86-81037-77-5

Skup
The Second Serbian International Conference on Applied Artificial Intelligence (AAI 2023)

Mjesto i datum
Kragujevac, Srbija, 19.05.2023. - 20.05.2023

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
early fire detection ; machine learning ; object detection ; semantic segmentation

Sažetak
Fire is an event that can be extremely dangerous since it can result in human casualties, as well as catastrophic property damage. To reduce the amount of damage that fires can cause, early fire detection and prompt coordination with the appropriate authorities are necessary. This issue has inspired a number of works on fire detection systems, which can be categorized into two groups: sensor-based technologies and image-based techniques. Image-based systems employing machine learning algorithms have been demonstrated to be superior to sensor-based systems due to faster fire detection and additional fire-related information they provide. The information includes the fire’s location, intensity, and progression. It is challenging for image-based systems to reliably detect the shape and limits of flames due to several factors, including a background, varying sizes of the fires, and interference from objects that resemble flames. In this study, our objective is to evaluate the performance of semantic segmentation and object detection models using a custom-made image dataset which contains fire in both indoor and outdoor environments.

Izvorni jezik
Engleski

Znanstvena područja
Interdisciplinarne tehničke znanosti, Informacijske i komunikacijske znanosti

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