aiqbench analysis
This research presents a specialized deep learning framework using Walsh-Hadamard transforms and domain adaptation to detect the unique subsurface burning characteristics of peatland fires. By leveraging transfer learning from general wildfire datasets, the model achieves high accuracy with reduced computational cost, addressing a critical gap in current fire detection systems that often fail on smoldering events.
Toward Generalizable Deep Learning Based Peatland Fire Detection via Walsh Hadamard Transform and Domain Adaptation
arXiv:2603.02465v2 Announce Type: replace-cross Abstract: 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 reparameterized into an equivalent inference model without sacrificing detection performance. Furthermore, the proposed framework leverages wildfire-to-peatland domain adaptation through transfer learning and introduces a mixed-domain training strategy that produces a unified detector capable of recognizing both wildfire and peatland fire events. Experimental results demonstrate that transfer learning substantially improves peatland fire detection under limited-data conditions, while the proposed WHT-ResNet-50 achieves higher accuracy and F1-score than conventional architectures with fewer parameters. The structurally reparameterized model further reduces inference cost while preserving detection accuracy. Video-based evaluation demonstrates robust performance with low false alarm rates, achieving a 100\% event detection rate across all positive test videos. Overall, the proposed framework provides an accurate, efficient, and practical solution for early peatland fire detection.
Abstract: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 reparameterized into an equivalent inference model without sacrificing detection performance. Furthermore, the proposed framework leverages wildfire-to-peatland domain adaptation through transfer learning and introduces a mixed-domain training strategy that produces a unified detector capable of recognizing both wildfire and peatland fire events. Experimental results demonstrate that transfer learning substantially improves peatland fire detection under limited-data conditions, while the proposed WHT-ResNet-50 achieves higher accuracy and F1-score than conventional architectures with fewer parameters. The structurally reparameterized model further reduces inference cost while preserving detection accuracy. Video-based evaluation demonstrates robust performance with low false alarm rates, achieving a 100\% event detection rate across all positive test videos. Overall, the proposed framework provides an accurate, efficient, and practical solution for early peatland fire detection.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2603.02465 [cs.CV]
(or arXiv:2603.02465v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2603.02465 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 300 words
