Abstract
Machine learning (ML)-based wildfire detection methods have been developed in recent years, primarily using deep learning (DL) models trained on large collections of wildfire images and videos. However, peatland fires exhibit distinct visual and physical characteristics -- such as smoldering combustion, low flame intensity, persistent smoke, and subsurface burning -- that limit the effectiveness of conventional wildfire detectors trained on open-flame forest fires. In this work, we present a transfer learning-based approach for peatland fire detection that leverages knowledge learned from general wildfire imagery and adapts it to the peatland fire domain. We initialize a DL-based peatland fire detector using pretrained weights from a conventional wildfire detection model and subsequently fine-tune the network using a dataset composed of Malaysian peatland images and videos. This strategy enables effective learning despite the limited availability of labeled peatland fire data. Experimental results demonstrate that transfer learning significantly improves detection accuracy and robustness compared to training from scratch, particularly under challenging conditions such as low-contrast smoke, partial occlusions, and variable illumination. The proposed approach provides a practical and scalable solution for early peatland fire detection and has the potential to support real-time monitoring systems for fire prevention and environmental protection.
Abstract (translated)
近年来，基于机器学习（ML）的野火检测方法主要通过深度学习（DL）模型，在大量野火图像和视频上进行训练而开发。然而，泥炭地火灾具有独特的视觉和物理特性，例如阴燃、火焰强度低、持续冒烟以及地下燃烧等特征，这限制了基于传统森林大火数据集训练的常规火灾检测器的有效性。 在本研究中，我们提出了一种基于迁移学习的方法来检测泥炭地火灾。该方法利用从一般野火图像和视频中学到的知识，并将其应用于专门针对泥炭地火灾的情境。我们通过使用预先训练好的传统野火检测模型的权重初始化一个DL基础的泥炭地火灾探测器，然后用包含马来西亚泥炭地图像和视频的数据集进行微调。这种方法在标记数据有限的情况下也能实现有效学习。 实验结果显示，在对比度低、部分遮挡及光照变化等复杂条件下，与从头开始训练相比，迁移学习显著提高了检测准确性和鲁棒性。所提出的方法为早期泥炭地火灾检测提供了一种实用且可扩展的解决方案，并有可能支持用于防火和环境保护的实时监控系统。
URL
https://arxiv.org/abs/2603.02465
