SFFR：面向多光谱航空目标检测的空间-频率特征重建方法
SFFR: Spatial-Frequency Feature Reconstruction for Multispectral Aerial Object Detection
AI总结：
针对现有多光谱航空目标检测未充分挖掘频域特征潜力的问题，提出基于KAN的SFFR方法，通过FCEKAN与MSGKAN模块实现空频域特征重建，在多个无人机数据集上取得更优检测性能。
摘要：
近期的多光谱目标检测方法主要聚焦于基于卷积神经网络（CNNs）或Transformer的空间域特征融合，而频率域特征的潜力尚未得到充分挖掘。本研究提出了一种全新的空间与频率特征重建方法SFFR，该方法利用Kolmogorov-Arnold Network（KAN，科尔莫戈罗夫-阿诺德网络）的空间-频率特征表示机制，在特征融合前重建空间域与频率域的互补表征。SFFR的核心组件为提出的频率分量交换KAN（FCEKAN）模块与多尺度高斯KAN（MSGKAN）模块。FCEKAN引入了创新的选择性频率分量交换策略，基于RGB与红外（IR）图像的频率特征，有效提升跨模态特征的互补性与一致性。MSGKAN模块在空间域展现出优异的非线性特征建模能力，通过利用多尺度高斯基函数，它能够有效捕捉不同无人机飞行高度下尺度变化带来的特征差异，显著增强模型对尺度变化的适应性与鲁棒性。实验验证表明，本研究提出的FCEKAN与MSGKAN模块具备互补性，可分别有效捕捉频率与空间语义特征，从而实现更优的特征融合。在SeaDroneSee、DroneVehicle与DVTOD数据集上开展的大量实验证明，所提方法在无人机多光谱目标感知任务上具备优越性能与显著优势。代码将开源至https://github.com/qchenyu1027/SFFR。
英文摘要：
Recent multispectral object detection methods have primarily focused on spatial-domain feature fusion based on CNNs or Transformers, while the potential of frequency-domain feature remains underexplored. In this work, we propose a novel Spatial and Frequency Feature Reconstruction method (SFFR) method, which leverages the spatial-frequency feature representation mechanisms of the Kolmogorov-Arnold Network (KAN) to reconstruct complementary representations in both spatial and frequency domains prior to feature fusion. The core components of SFFR are the proposed Frequency Component Exchange KAN (FCEKAN) module and Multi-Scale Gaussian KAN (MSGKAN) module. The FCEKAN introduces an innovative selective frequency component exchange strategy that effectively enhances the complementarity and consistency of cross-modal features based on the frequency feature of RGB and IR images. The MSGKAN module demonstrates excellent nonlinear feature modeling capability in the spatial domain. By leveraging multi-scale Gaussian basis functions, it effectively captures the feature variations caused by scale changes at different UAV flight altitudes, significantly enhancing the model's adaptability and robustness to scale variations. It is experimentally validated that our proposed FCEKAN and MSGKAN modules are complementary and can effectively capture the frequency and spatial semantic features respectively for better feature fusion. Extensive experiments on the SeaDroneSee, DroneVehicle and DVTOD datasets demonstrate the superior performance and significant advantages of the proposed method in UAV multispectral object perception task. Code will be available at https://github.com/qchenyu1027/SFFR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
