2024/11/12 by Aditya Jonnalagadda, Aditya V. Jonnalagadda, Hashim A. Hashim +5 · 1 voice · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Fire Detection and Safety Systems #Video Surveillance and Tracking Methods #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2411.08171
openalex publication_date 2024/11/12 · arxiv published 2024/11/12 · arxiv updated 2024/11/12 · openalex created_date 2024/11/16 · openalex updated_date 2026/08/01
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