A DEEP LEARNING BASED EXPERIMENT ON FOREST WILDFIRE DETECTION IN MACHINE VISION COURSE

Abstract

Forest wildfires pose a significant threat to ecosystems, wildlife, human life, and property, causing severe environmental and economic damage worldwide. Early detection of wildfires is crucial to minimize their impact and enable timely intervention. Traditional wildfire detection methods, such as satellite monitoring and manual surveillance, often suffer from delays, limited accuracy, and high operational costs. To address these challenges, this project proposes a deep learning-based approach for forest wildfire detection using machine vision techniques. The system leverages Convolutional Neural Networks (CNNs) to automatically identify fire and smoke patterns from images and video streams in real time. The proposed model is trained on a dataset containing labeled images of forest environments with and without wildfire occurrences. Preprocessing techniques such as image resizing, normalization, and data augmentation are applied to improve model performance and generalization. The deep learning model extracts spatial features from input images and classifies them into fire or non-fire categories with high accuracy. Additionally, the system can be integrated with surveillance cameras or drones for continuous monitoring of forest areas. The use of machine vision enables faster detection compared to traditional approaches, significantly reducing response time. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, demonstrating its effectiveness in identifying wildfire events under different environmental conditions. Experimental results indicate that the proposed system achieves high detection accuracy and robustness, even in challenging scenarios such as varying lighting and smoke density. This project highlights the potential of deep learning and computer vision in enhancing disaster management systems. By providing an automated, efficient, and scalable solution, the system contributes to early wildfire detection and prevention, ultimately helping to protect natural resources and human lives.