Forest Fire Detection

Forest fire detection research focuses on developing rapid and accurate systems for identifying fires, crucial for minimizing damage and saving lives. Current efforts concentrate on improving the efficiency and accuracy of deep learning models, particularly convolutional neural networks (CNNs) like YOLO variants and U-Net, often incorporating attention mechanisms and optimized for resource-constrained environments or specific data types like UAV imagery. These advancements leverage techniques such as transfer learning, improved feature extraction, and refined loss functions to enhance detection rates and reduce false alarms, impacting both ecological conservation and disaster management. The integration of these models with sensor networks and decision support systems further aims to improve real-time fire detection and response.