Forecasting Fire Using MobileNet Architecture

The introduction of fire has been both beneficial and disastrous to the natural world and humanity. Many disastrous fires, both natural and man-made, have occurred throughout history. Numerous lives were lost, along with property and money. Numerous innovations have been made in the areas of fire detection, fire suppression, and alarm systems. All of these methods have advantages and disadvantages that can be improved upon in future fire detection innovations. Recent developments in deep learning technology have allowed for precise advancements in the field of vision. Image recognition, object detection, and categorization are all areas where machine learning techniques are put to use. When it comes to identifying and categorizing images, supervised algorithm have made a significant leap forward. In this paper, the strengths and weaknesses of several models for fire prediction, including Random Forest, the Support Vector Machine using MobilenetV2 architecture is discussed.