Deep Learning Technique for Real-Time Forest Fire Detection from Video Streams

Forest fires are currently the most common issue that we are dealing with. The resources that have been lost are irreplaceable. Forest fires are uncontrollable fires that occur in nature as a result of human involvement or any other natural event. Forest fires can be so large that putting them out takes a long time. Contrasting with previous years, there has been a striking surge in the frequency of forest fires. Changes in climatic conditions are the key factor affecting the scope of forest fires. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. Long dry spells in the spring and summer make the woodlands particularly vulnerable. The extent of forest fires is mostly determined by weather conditions such as precipitation and wind, as well as the terrain's architecture. The incorporation of a Video Fire Detection model significantly enhances the efficiency of fire detection systems, particularly in large spaces. Establishing a video fire detection system involves focusing on spatial, spectral, and temporal indicators, as well as flame picture segmentation, recognition, tracking, and prediction. Analytical techniques, specifically utilizing ResNet50 and VGG16 models, can be applied to identify pool fire images through these segmentation processes, contributing to more robust fire detection capabilities.