FMC-DETR：频率解耦多域协调用于航拍物体检测
FMC-DETR: Frequency-Decoupled Multi-Domain Coordination for Aerial-View Object Detection
AI总结：
本文提出FMC-DETR框架，通过波let Kolmogorov-Arnold Transformer增强全局结构感知，多域特征协调模块优化跨尺度特征，以及紧凑部分融合模块提升特征多样性，实现航拍场景下小目标检测的高性能。
摘要：
遥感目标检测是自然资源监测、交通管理及无人机救援等实际应用中的关键技术。在高分辨率航拍图像中检测小目标仍然具有挑战性，因为弱视觉线索和复杂场景中全局上下文建模不足。现有方法常面临延迟的上下文交互和有限的非线性推理能力，限制了其有效细化浅层表示的能力，最终导致性能不佳。为了解决这些挑战，我们提出了FMC-DETR，一种用于航拍目标检测的频率解耦融合框架。首先，我们提出了波let Kolmogorov-Arnold Transformer（WeKat）主干网络，该网络采用级联波let变换来增强浅层特征中的全局低频结构感知，同时保留细粒度细节，并进一步利用Kolmogorov-Arnold网络对多尺度依赖关系进行自适应非线性建模。其次，我们引入了多域特征协调（MDFC）模块，通过部分通道空间、光谱和结构协调来优化跨尺度融合的表示，从而在嘈杂场景中加强与小目标相关的特征响应。最后，我们设计了紧凑部分融合（CPF）模块，通过紧凑多分支聚合和逐步部分细化来提升特征多样性和多尺度交互，同时保持稳定的信息流并减少冗余扰动。在多个遥感基准测试中，FMC-DETR实现了最先进的性能，并显著优于基线检测器。代码可在https://github.com/bloomingvision/FMC-DETR上获取。
英文摘要：
Remote sensing object detection is a critical technology for real-world applications such as natural resource monitoring, traffic management, and UAV-based rescue. Detecting tiny objects in high-resolution aerial imagery remains challenging due to weak visual cues and insufficient global context modeling in complex scenes. Existing methods often suffer from delayed contextual interaction and limited nonlinear reasoning, which restrict their ability to effectively refine shallow representations and ultimately lead to suboptimal performance. To address these challenges, we propose FMC-DETR, a frequency-decoupled fusion framework for aerial-view object detection. First, we propose the Wavelet Kolmogorov-Arnold Transformer (WeKat) backbone, which employs cascaded wavelet transforms to enhance global low-frequency structure perception in shallow features while preserving fine-grained details, and further leverages Kolmogorov-Arnold networks for adaptive nonlinear modeling of multi-scale dependencies. Second, we introduce the Multi-Domain Feature Coordination (MDFC) module, which refines cross-scale fused representations through partial-channel spatial, spectral, and structural coordination, thereby strengthening small-object-related feature responses in cluttered scenes. Finally, we design the Compact Partial Fusion (CPF) module, which performs compact multi-branch aggregation with progressive partial refinement to improve feature diversity and multi-scale interaction while preserving stable information flow and reducing redundant perturbation. Extensive experiments across multiple remote sensing benchmarks demonstrate that FMC-DETR achieves state-of-the-art performance and significantly outperforming the baseline detector. Code is available at https://github.com/bloomingvision/FMC-DETR.
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。
