Wild Fire Detection Using Deep Learning

Forest fires are a caused by many factors like dry rainfall conditions, climate change, high temperatures, lightning strikes. Forest Wildfire are major problem that causes harm to people, the environment, and the economy. To reduce the damage, its essential to detect wildfires early. Traditional methods like CNN and SVM have been used, but they have limitations such as low accuracy and requiring large amounts of data. To overcome these challenges, this project proposes using a deep learning approach called Reduced VGGNet for Wildfire Classification, which can automatically extract features from images and improve detection accuracy. The project aims to design a system that can detect wildfires and wildfire regions efficiently and effectively. The Vibe algorithm is used and bounding boxes are generated to detect the Wildfire region. Our proposed solution aims to provide a more efficient, scalable and cost-effective approach to addressing the problem. Our goal is to provide a reliable and efficient solution that can make a meaningful impact in addressing the problem and we’re confident that our proposed framework can achieve this.