Fire Detection Methods Based on Various Color Spaces and Gaussian Mixture Models

• Autorzy: Munshi Amr

Identyfikatory

DOI

10.12913/22998624/138924

• Języki publikacji: EN

Abstrakty

EN

Fire disasters are very serious problems that may cause damages to ecological systems, infrastructure, properties, and even a threat to human lives; therefore, detecting fires at their earliest stage is of importance. Inspired by the technological advancements in artificial intelligence and image processing in solving problems in different applications, this encourages adopting those technologies in reducing the damage and harm caused by fire. This study attempts to propose an intelligent fire detection method by investigating three approaches to detect fire based on three different color models: RGB, YCbCr, and HSV are presented. The RGB method is applied based on the relationship among the red, green and blue values of pixels in images. In the YCbCr color model, image processing and machine learning techniques are used for morphological processing and automatic recognition of fire images. Whereas for the HSV supervised machine learning techniques are adopted, namely decision rule and Gaussian mixture model (GMM). Further, the expectation maximization (EM) algorithm is deployed for the GMM parameters estimation. The three proposed models were tested on two data sets, one of which contains fire images, the other consists of non-fire images with some having fire-like colors to test the efficiency of the proposed methods. The experimental results showed that the overall accuracies on two data sets for the RGB, YCbCr, and HSV methods were satisfactory and were efficient in detecting outdoor and indoor fires.

Słowa kluczowe

EN

fire detection; fire pixels; fire-like pixels; color models; Gaussian mixture models

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2021
• Tom: Vol. 15, no. 3
• Strony: 197--214
• Opis fizyczny: Bibliogr. 15 poz., fig., tab.

Twórcy

autor

Munshi Amr

• Computer Engineering Department, Umm Al-Qura University, Saudi Arabia

• https://orcid.org/0000-0002-4002-3755

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).