FF-fusion: A knowledge-distilled lightweight visible–infrared image fusion framework for Forest fire monitoring

作者：Qian Yu, Gui Zhang, Ying Wang, XC Wu, Wenping Liu, Zhuo Ouyang, Zhi Jiang, Long Qi, Wuyang Hu · 发表于：Ecological Informatics · 年份：2026 · DOI：10.1016/j.ecoinf.2026.103952 · 研究领域：Fire Detection and Safety Systems、Fire effects on ecosystems、Image Enhancement Techniques

Visible–infrared image fusion offers an effective strategy for forest fire monitoring by integrating complementary information from visible-light and thermal-infrared modalities, particularly under smoke interference, low illumination, and complex forest backgrounds. However, forest-fire-specific paired datasets remain scarce, and lightweight fusion methods have seldom been evaluated under real-world monitoring scenarios and edge-deployment constraints. To address these limitations, this study constructs TF-1770, a spatially aligned visible–infrared forest fire dataset collected from ground-based and UAV-based firefighting scenes. The dataset comprises paired visible-light and thermal-infrared images captured under diverse viewpoints, smoke densities, illumination conditions, and target scales. Based on TF-1770, we propose FF-Fusion, a lightweight visible–infrared image fusion framework based on knowledge distillation. The teacher network learns robust multimodal representations through wavelet-based fusion and cross-modal feature modeling, while the student network incorporates re-parameterizable convolution blocks to enable efficient edge inference. Experimental results on TF-1770 show that FF-Fusion achieves entropy, standard deviation, average gradient, visual information fidelity, and edge information transfer metric values of 7.10, 43.66, 10.83, 0.649, and 0.584, respectively. The deployable student model maintains a compact size of only 1.34 MB. Downstream detection ex...