用VGG16模型提升火灾检测准确率，助力早期预警
Revolutionizing Wildfire Detection with Convolutional Neural Networks: A VGG16 Model Approach
- 基于VGG16构建卷积神经网络，结合数据增强解决样本不平衡
- 在D-FIRE数据集上实现低误报率，有效减少漏检
- 适合需要实时火灾监测的应急管理部门使用
2024年仅美国就记录了超过8,024起野火事件，造成数千人死亡及重大基础设施与生态破坏。野火频发且加剧，亟需高效预警系统以避免灾难性后果。本研究旨在通过基于VGG16架构的卷积神经网络（CNN）提升野火检测精度。采用包含多种野火与非野火图像的D-FIRE数据集，针对低分辨率图像、数据集不平衡及实时应用需求等挑战，通过数据增强技术扩充数据，并优化VGG16模型用于二分类。模型表现出极低的误报率，对减少未被发现的火灾至关重要。结果表明，深度学习模型如VGG16可为早期野火识别提供可靠、自动化方案，帮助相关部门快速响应。未来工作将聚焦于接入实时监控网络，并扩展数据集以覆盖更多样化的火灾场景。
原文摘要 · Abstract (English)
Over 8,024 wildfire incidents have been documented in 2024 alone, affecting thousands of fatalities and significant damage to infrastructure and ecosystems. Wildfires in the United States have inflicted devastating losses. Wildfires are becoming more frequent and intense, which highlights how urgently efficient warning systems are needed to avoid disastrous outcomes. The goal of this study is to enhance the accuracy of wildfire detection by using Convolutional Neural Network (CNN) built on the VGG16 architecture. The D-FIRE dataset, which includes several kinds of wildfire and non-wildfire images, was employed in the study. Low-resolution images, dataset imbalance, and the necessity for real-time applicability are some of the main challenges. These problems were resolved by enriching the dataset using data augmentation techniques and optimizing the VGG16 model for binary classification. The model produced a low false negative rate, which is essential for reducing unexplored fires, despite dataset boundaries. In order to help authorities execute fast responses, this work shows that deep learning models such as VGG16 can offer a reliable, automated approach for early wildfire recognition. For the purpose of reducing the impact of wildfires, our future work will concentrate on connecting to systems with real-time surveillance networks and enlarging the dataset to cover more varied fire situations.
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