Wildfire Detection Using Vision Transformer(ViT)

Global temperatures have risen over the last few decades, disrupting nature's typical balance. As temperatures rise, wildfires have devastated millions of acres of land, as well as thousands of structures and homes. Wildfires emit pollution and hazardous fumes that travel thousands of miles, endangering lives all over the world. The majority of wildfires are caused by human activity, which cannot be predicted merely based on climate conditions. To detect wildfires before they spread, we offer a wildfire detection system coupled with an end-to-end Vision Transformer (ViT) image classification model trained on a wildfire imagery dataset to detect potential flames or smoke in an image. In addition, our approach also acquires the weather data and the intensity of the fire. Contrasting with existing wildfire detection systems, our proposed solution is a fusion of the Internet of Things (IoT) and Deep Learning, aiming to provide a one-stop solution for all the needs required to minimize the damage caused by wildfires. When validated and tested using various benchmark datasets, video surveillance acquired a high accuracy of 92.89% with minimal computational power.