Real Time Fire Detection using Color Probability Segmentation and DenseNet Model for Classifier

Adhinata, Faisal Dharma and Ramadhan, Nur Ghaniaviyanto Real Time Fire Detection using Color Probability Segmentation and DenseNet Model for Classifier. (IJACSA) International Journal of Advanced Computer Science and Applications.

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The forest is an outdoor environment not touched by the surrounding community, so it is not immediately handled when a fire occurs. Therefore, surveillance using cameras is needed to see the presence of fire hotspots in the forest. This study aims to detect hotspots through video data. As is known, fire has a variety of colors, ranging from yellow to reddish. The segmentation process requires a method that can recognize various fire colors to get a candidate fire object area in the video frame. The methods used for the color segmentation process are Gaussian Mixture Model (GMM) and Expectation–maximization (EM). The segmentation results are candidates for fire areas, which in the experiment used the value of K=4. This fire object candidate needs to be ascertained whether the segmented object is a fire object or another object. In the feature extraction stage, this research uses the DenseNet-169 or DenseNet-201 models. In this study, various color tests were carried out, namely RGB, HSV, and YCbCr. The test results show that RGB color produces the most optimal training accuracy. This RGB color configuration is used to test using video data. The test results show that the true positive and false negative values are quite good, 98.69% and 1.305%. This video data processing produces fps with an average of 14.43. So, it can be said that this combination of methods can be used to process real time data in case studies of fire detection.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Informatics > Software Engineering
Depositing User: Faisal Dharma Adhinata, S.Kom., M.Cs.
Date Deposited: 09 Feb 2023 03:19
Last Modified: 11 Feb 2023 00:08

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