Abstract: The high-precision coupling and docking technology between tanker and receiver aircraft plays a critical role in tasks such as collaborative formation and drone recovery. Traditional manual docking methods face challenges such as high complexity, low success rates, and poor reliability, while the precise recognition of the drogue is a key factor for the future of intelligent docking technologies. Existing drogue detection methods mostly rely on anchor box or point initialization and post-processing techniques such as non-maximum suppression, which leads to issues of low efficiency, high debugging costs, and poor robustness when deployed on embedded platforms. While DETR-based detectors offer end-to-end capabilities, they suffer from slow inference and weak performance with small targets. This paper proposes Drogue-DETR, a real-time drogue detector for embedded platforms. It introduces an adaptive region selection attention mechanism to reduce computational complexity, enhances feature extraction efficiency, and incorporates a frequency-domain filtering module to retain small target edges while suppressing background noise. Additionally, a multi-scale attention aggregation module improves contextual understanding and boosts detection robustness. Experimental results on a custom drogue dataset and VisDrone dataset show that Drogue-DETR outperforms existing methods, meeting the requirements for embedded airborne devices.

摘要： 母机与靶机的高精度耦合对接技术在协同编队、无人机回收等任务中具有重要的战略价值。传统的手动对接操作方式面临任务难度大、成功率低和可靠性差等问题，而锥套目标的精准识别则是未来智能化耦合对接技术中的关键。现有的针对锥套目标的检测方法大都依赖于锚框、锚点的初始化以及非极大值抑制等后处理步骤，使得算法在机载嵌入式平台上部署时存在效率低，调试成本高且鲁棒性差的困难。基于DETR的检测器尽管具备端到端的检测能力，但存在推理延迟大，对弱小的锥套目标检测效果差的缺陷。本文提出了一种面向嵌入式平台实时锥套目标检测算法Drogue-DETR。该算法通过引入自适应区域选择注意力机制，降低计算复杂度，提升顶层特征提取效率，并设计了频域特征筛选模块，保留小目标边缘特征、抑制背景干扰。此外，构建的提出的多尺度注意力聚合模块深入挖掘上下文信息，进一步提升了检测的鲁棒性。在构建的锥套数据集和VisDrone数据集上的实验结果表明，Drogue-DETR优于现有的主流检测算法，满足嵌入式机载设备的使用要求。