Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2403
Title: Fire Detection in Images Using Framework Based on Image Processing, Motion Detection and Convolutional Neural Network
Authors: Taşpınar, Yavuz Selim
Köklü, Murat
Altın, Mustafa
Keywords: Fire detection
Flame detection
Image processing
Motion Detection
Transfer learning
Issue Date: 2021
Publisher: Ismail Saritas
Abstract: Fire detection in images has been frequently used recently to detect fire at an early stage. These methods play an important role in reducing the loss of life and property. Fire is not only chemically complex, but also physically very complex. The shape and color of the flame varies according to the type of fuel in the fire. This has made fire detection a very challenging problem. Advanced image processing algorithms are also needed to accurately detect fire. To solve this problem, a three-stage fire framework was created in this study. In the first stage, the flame region was extracted from the images containing the fire region with the basic image processing algorithms. At this stage, reduce brightness, HSL, YCbCr, median and herbaceous filters are applied successively to the image. Since the flame image has a polygonal structure by nature, the number of edges of the flame region has been found. In the second stage, the mobility feature of the flame was utilized. For this purpose, the mobility of the flame was determined by comparing the video frames containing the fire image. The CNN method was used to detect the fire in the images. The CNN model was trained with the transfer learning method using the Inception V3, SequeezeNet, VGG16 and VGG19 trained models. As a result of the tests of the models, 98.8%, 97.0%, 97.3% and 96.8% classification success were obtained, respectively. With the proposed fire detection framework, it is thought that the damage caused by the fire can be reduced by early detection of the fire and timely intervention. © 2021, Ismail Saritas. All rights reserved.
URI: https://doi.org/10.18201/IJISAE.2021473636
https://hdl.handle.net/20.500.13091/2403
ISSN: 2147-6799
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
Teknik Bilimler Meslek Yüksekokulu Koleskiyonu

Files in This Item:
File SizeFormat 
document (90).pdf237.83 kBAdobe PDFView/Open
Show full item record

CORE Recommender

Page view(s)

40
checked on Jan 30, 2023

Download(s)

20
checked on Jan 30, 2023

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.