Toward Generalizable Deep Learning Based Peatland Fire Detection via Walsh Hadamard Transform and Domain Adaptation
Machine learning-based wildfire detection has advanced significantly using deep learning models trained on large wildfire image and video datasets. However, peatland fires exhibit distinct characteristics, including smoldering combustion, low flame intensity, persistent smoke, and subsurface burning, limiting the effectiveness of conventional wildfire detectors. To address these challenges, we propose an efficient deep learning framework for peatland fire detection based on a Walsh--Hadamard Transform enhanced ResNet-50 (WHT-ResNet-50), which improves feature representation while reducing model complexity. To enable efficient deployment, the training-time architecture is structurally repa...
| Authors | Emadeldeen Hamdan, Ahmad Faiz Tharima, Mohd Zahirasri Mohd Tohir et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 March 2026 |
| Last updated at source | 31 August 2026 |
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