FireRGBTNet: A Lightweight Forest Fire Detection Model Based on Efficient RGB–Thermal Fusion

In recent years, UAV-based visible and thermal (RGB-T) multimodal object detection has demonstrated significant potential for monitoring forest fires in complex environments. However, restricted by the intrinsic discrepancies between modalities, existing RGB-T models still struggle to achieve optimal detection accuracy. To address the aforementioned issues, this paper proposes FireRGBTNet, a lightweight and efficient RGB-T fusion model for UAV-based forest fire detection. First, a heterogeneous dual-stream backbone is designed to precisely capture modality-specific information via customized feature extraction modules, utilizing Target-Enhanced Downsampling to adaptively preserve the fragile features of small targets. Second, a multi-scale semantic alignment enhancement branch is proposed to explicitly bridge the semantic gap through the dual constraints of statistical distribution and semantic direction. Finally, a multimodal spatial gated fusion module is constructed, which utilizes spatial gated interactions to effectively suppress multimodal noise and incorporates Mish Gated Linear Units to compensate for global semantics, thereby achieving high-quality heterogeneous feature fusion. Extensive experiments on the RGBT-3M dataset demonstrate that with a mere 4.13M parameters, FireRGBTNet achieves accuracies of 95.9% and 62.8% in terms of mAP@0.5 and mAP@0.5:0.95, respectively. The proposed model achieves an optimal trade-off between detection accuracy and computational efficiency, providing a highly effective solution for UAV-based forest fire monitoring in complex environments.