An Optimum Classifier Model with Fuzzy C-Means for Fire Detection Technology

Abstract

Flames recognition methodology is most important for completely diminishing the flame losses in different fired environmental conditions. However, there is delayed detection and lower accuracy in the various common detection methods. Thus, optimum image/video fire detection technology is proposed in this paper based on a support vector machine (SVM) with the fuzzy c-mean, discrete wavelet transform (DWT), and gray level co-occurrence matrices (GLCM) feature extraction for the detection of fires. This algorithm has been tested on various fire and non-fire images for classification accuracy. A performance evaluation of the proposed classifier algorithm and existing algorithms is compared, showing that the accuracy and other metrics of the proposed classifier algorithm are higher than other algorithms. Furthermore, simulation results show that the proposed classifier model is improved the forecast detection accuracy of fires.

Fire performance of Pinus taeda wood treated with zinc borate before and after leaching

“…Carbonization is the transformation of the surface and ultrastructure of wood in response to temperature (Ali et al 2019). It can be measured through carbonization area (Ca)(cm 2 ) and carbonization index (Ci)(%).…”

“…SEM analysis is used to study the micromorphology, surface topology and ultrastructure surface of the wood (Ali et al 2019).…”

“…The first stage pyrolysis begins as the wood heats up, generating volatile gases, coal and tar (liquid residue), which then, in the presence of an ignition source, react with oxygen combustion releasing carbon monoxide, carbon dioxide and water. This exothermic reaction keeps pyrolysis going and generates more volatiles, and continues until the wood is fully converted to coal and no volatiles remain (Ali et al 2019, Lowden and Hull 2013, Mensah et al 2023.…”