Unmanned aerial vehicle (UAV)-based bi-modal salient object detection (BSOD) aims to segment salient objects in a scene utilizing complementary cues in unaligned RGB and thermal image pairs. However, the high computational expense of existing UAV-based BSOD models limits their applicability to real-world UAV devices. To address this problem, we propose an efficient Fourier filter network with contrastive learning that achieves both real-time and accurate performance. Specifically, we first design a semantic contrastive alignment loss to align the two modalities at the semantic level, which facilitates mutual refinement in a parameter-free way. Second, inspired by the fast Fourier transform that obtains global relevance in linear complexity, we propose synchronized alignment fusion, which aligns and fuses bi-modal features in the channel and spatial dimensions by a hierarchical filtering mechanism. Our proposed model, AlignSal, reduces the number of parameters by 70.0%, decreases the floating point operations by 49.4%, and increases the inference speed by 152.5% compared to the cutting-edge BSOD model (i.e., MROS). Extensive experiments on the UAV RGB-T 2400 and three weakly aligned datasets demonstrate that AlignSal achieves both real-time inference speed and better performance and generalizability compared to sixteen state-of-the-art BSOD models across most evaluation metrics. In addition, our ablation studies further verify AlignSal's potential in boosting the performance of existing aligned BSOD models on UAV-based unaligned data. The code is available at: https://github.com/JoshuaLPF/AlignSal.

本研究针对无人机（UAV）基于双模显著物体检测（BSOD）中模型计算开销高的问题，提出了一种高效的傅里叶过滤网络，通过对比学习实现实时和准确的检测。关键创新在于设计了语义对比对齐损失，提升了两种模态的对齐效果，同时采用同步对齐融合方法，大幅减少了参数数量和计算量，实验证明该方法在速度和性能上优于现有的最先进模型。

基于对比学习的高效傅里叶过滤网络用于无人机非对齐双模显著物体检测