Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection

TL;DR

This study compares transfer learning models with custom-built CNNs for wildfire detection, analyzing their effectiveness across various metrics to determine the best approach for complex wildfire imagery.

Contribution

The paper provides a comprehensive comparison of transfer learning and custom CNN models specifically for wildfire detection, highlighting their relative performance and practical implications.

Findings

Transfer learning models generally outperform custom-built models in accuracy.

VGG-19 and ResNet101 achieve the highest precision and recall.

Custom models like VGG-7 and CNN-SVM show competitive results in specific scenarios.

Abstract

Contemporary Artificial Intelligence (AI) and Machine Learning (ML) research places a significant emphasis on transfer learning, showcasing its transformative potential in enhancing model performance across diverse domains. This paper examines the efficiency and effectiveness of transfer learning in the context of wildfire detection. Three purpose-built models -- Visual Geometry Group (VGG)-7, VGG-10, and Convolutional Neural Network (CNN)-Support Vector Machine(SVM) CNN-SVM -- are rigorously compared with three pretrained models -- VGG-16, VGG-19, and Residual Neural Network (ResNet) ResNet101. We trained and evaluated these models using a dataset that captures the complexities of wildfires, incorporating variables such as varying lighting conditions, time of day, and diverse terrains. The objective is to discern how transfer learning performs against models trained from scratch in addressing the intricacies of the wildfire detection problem. By assessing the performance metrics, including accuracy, precision, recall, and F1 score, a comprehensive understanding of the advantages and disadvantages of transfer learning in this specific domain is obtained. This study contributes valuable insights to the ongoing discourse, guiding future directions in AI and ML research. Keywords: Wildfire prediction, deep learning, machine learning fire, detection