Forest Fire Detection with Combined SVM and Deep CNN Approach

Abstract

Our proposed approach for forest fire detection presents a significant advancement over existing techniques. By integrating SVM-based classification with state-of-the-art deep CNN architectures, specifically VGG16 and ResNet50, we achieve outstanding accuracy. Notably, our method attains a remarkable 97.21% training set accuracy on ResNet-50. This exceptional accuracy enhances early fire prediction and minimizes false alarm rates, contributing significantly to environmental conservation and human life preservation. Moreover, our adept utilization of fine-tuning techniques effectively addresses challenges related to poor generalization and overfitting, thereby further enhancing the overall efficacy of our innovative approach.