对比了迁移学习与自建模型在野火检测中的表现。
Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection
- 用VGG、CNN-SVM等模型与预训练模型对比
- 预训练模型准确率最高达98.7%
- 适合做野火检测的AI研究者参考
当前人工智能与机器学习研究高度关注迁移学习，展现其在提升多领域模型性能方面的变革潜力。本文评估了迁移学习在野火检测中的效率与效果。将三个自建模型——VGG-7、VGG-10和CNN-SVM——与三个预训练模型——VGG-16、VGG-19和ResNet101——进行严格比较。实验基于包含复杂因素（如光照变化、昼夜差异、多样地形）的野火数据集进行训练与评估。通过准确率、精确率、召回率及F1分数等指标，全面分析迁移学习相较于从零训练模型在应对野火检测挑战时的优势与局限。研究为该领域的未来方向提供了重要洞见。
原文摘要 · Abstract (English)
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
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