A Deep Learning-Based on Forest Wildfire Detection in Machine Vision Course

Forest wildfires are one of the most destructive natural disasters, causing severe environmental, economic, and ecological damage. Early detection plays a crucial role in preventing the rapid spread of fire and reducing losses. This project proposes a deep learning–based wildfire detection system integrated with machine vision techniques to automatically identify fire and smoke in forest environments. The system uses a trained convolutional neural network (CNN) model to analyze real-time video or image inputs and classify potential wildfire occurrences with high accuracy. The proposed model processes frames using feature extraction, pattern recognition, and probability-based classification to detect early signs of fire