Infrared unmanned aerial vehicle (UAV) target images often suffer from motion blur degradation caused by rapid sensor movement, significantly reducing contrast between target and background. Generally, detection performance heavily depends on the discriminative feature representation between target and background. Existing methods typically treat deblurring as a preprocessing step focused on visual quality, while neglecting the enhancement of task-relevant features crucial for detection. Improving feature representation for detection under blur conditions remains challenging. In this paper, we propose a novel Joint Feature-Domain Deblurring and Detection end-to-end framework, dubbed JFD3. We design a dual-branch architecture with shared weights, where the clear branch guides the blurred branch to enhance discriminative feature representation. Specifically, we first introduce a lightweight feature restoration network, where features from the clear branch serve as feature-level supervision to guide the blurred branch, thereby enhancing its distinctive capability for detection. We then propose a frequency structure guidance module that refines the structure prior from the restoration network and integrates it into shallow detection layers to enrich target structural information. Finally, a feature consistency self-supervised loss is imposed between the dual-branch detection backbones, driving the blurred branch to approximate the feature representations of the clear one. Wealso construct a benchmark, named IRBlurUAV, containing 30,000 simulated and 4,118 real infrared UAV target images with diverse motion blur. Extensive experiments on IRBlurUAV demonstrate that JFD3 achieves superior detection performance while maintaining real-time efficiency.

翻译：红外无人机（UAV）目标图像常因传感器快速运动而产生运动模糊退化，显著降低目标与背景之间的对比度。通常，检测性能高度依赖于目标与背景之间的判别性特征表示。现有方法通常将去模糊作为专注于视觉质量的预处理步骤，而忽视了对检测至关重要的任务相关特征的增强。在模糊条件下改进检测的特征表示仍具挑战性。本文提出一种新颖的联合特征域去模糊与检测端到端框架，称为JFD3。我们设计了一个权重共享的双分支架构，其中清晰分支引导模糊分支以增强判别性特征表示。具体而言，我们首先引入一个轻量级特征恢复网络，其中清晰分支的特征作为特征级监督来引导模糊分支，从而增强其检测的区分能力。随后，我们提出一个频率结构引导模块，该模块从恢复网络中提炼结构先验，并将其集成到浅层检测层中以丰富目标结构信息。最后，在双分支检测主干之间施加特征一致性自监督损失，驱动模糊分支逼近清晰分支的特征表示。我们还构建了一个名为IRBlurUAV的基准数据集，包含30,000张模拟和4,118张真实红外无人机目标图像，涵盖多种运动模糊。在IRBlurUAV上的大量实验表明，JFD3在保持实时效率的同时实现了卓越的检测性能。