Forest Fire Detection with Combined SVM and Deep CNN Approach

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 remarkabl...

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Bibliographic Details
Published in2024 2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC) pp. 1 - 6
Main Authors Ilyas, Bendjillali Ridha, Sofiane, Bendelhoum Mohamed, Abderrazak, Tadjeddine Ali, Miloud, Kamline, Kamila, Frioui, Boukenadil, Bahidja
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.05.2024
Subjects
Accuracy
Biological system modeling
CNN
Forest Fire Detection
Forestry
Real-time systems
Refining
ResNet50
Support vector machines
SVM
Training
VGG16
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Summary: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.
DOI:10.1109/ICEEAC61226.2024.10576417