Novel technique based on cascade classifiers for smoke image detection

Abstract

This article contributes a novel technique based on cascade classifiers for smoke detection by utilizing the image processing method. It has been a difficult issue for ten years or so due to its variety in shape, texture, and color. In this article, a machine learning methodology is represented to tackle this issue and simulated with MATLAB software. The smoke detection issue acted like a classification issue. The solution is demonstrated as a binary classification issue. Hence, the support vector machine (SVM) is represented for classification. In order to train and test the SVM classifier, both samples of positive and negative are gathered. Two SVM classifiers are utilized in the cascade. The first classifier distinguishes the presence of smoke if smoke presents in a provided input image; the second classifier is utilized to find the locale of smoke in a provided input image. The size of the window is set to 32 3 32 and slided across the whole image to identify the smoke in a zone of the window. The novel technique is a training dataset and utilizing linear kernel function. In this manner, the novel technique is tested with a test dataset. The first SVM classifier obtained 100% accuracy in training and 96% accuracy in testing. A training accuracy of 96% and a test accuracy of 93.6% were obtained by the second SVM classifier. This novel technique proved to be more proficient and cost-savvy than the traditional strategies.