Comprehensive Analysis of Forest Fire Detection using Deep Learning Models and Conventional Machine Learning Algorithms

Forest fire detection is a very challenging problem in the field of object detection. Fire detection-based image analysis have advantages such as usage on wide open areas, possibility for operator to visually confirm presence, intensity and size hazards, lower cost installation further exploitation. To overcome outdoors, deep learning conventional machine based computer vision techniques are employed determine when indoor systems not capable. In this work, we propose comprehensive forest using algorithms, techniques, hybrid models. Experiment results demonstrate that convolutional neural networks outperform other methods with 99.32% accuracy result.