发表机构
Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences; ShanghaiTech University; Beijing Institute of Basic Medical Sciences（中国科学院自动化研究所; 中国科学院大学; 上海科技大学; 北京基础医学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对红外弱小型无人机检测难题，提出Gaze-DETR探测器，通过学习内部优先级地图，经优先级头预测、特征调制和锚点查询注入等步骤，并采用多种监督方案训练，实验证明其能提供预定位指导，补充边界框监督。
AI 中文摘要
红外小目标检测具有挑战性，因为微小、低对比度目标易被杂波、噪声或遮挡掩盖。传统单帧和多帧探测器依赖边界框监督，对候选区域优先级或定位前保留弱目标证据缺乏明确指导。任务驱动视觉搜索可提供此类指导，基于此提出Gaze-DETR。它先通过优先级头预测归一化优先级地图，再经残差优先级引导特征调制增强高优先级响应并保留多尺度特征，最后通过优先级引导锚点查询注入将高优先级位置转换为解码器锚点查询。用三种监督方案训练优先级头，在TIR-UAV120-Gaze和Anti-UAV410数据集上实验，结果表明显式空间优先级学习能提供预定位指导，补充边界框监督。
英文摘要
Infrared small target detection (ISTD) remains challenging because tiny, low-contrast targets are easily overwhelmed by clutter, noise, or occlusion. Conventional single-frame and multi-frame detectors rely on bounding-box supervision, which specifies final target locations but offers little explicit guidance for prioritizing candidate regions or preserving weak-target evidence before localization. Task-driven visual search offers such guidance: top-down goals and visual evidence jointly form a spatial priority map that ranks candidate locations. Building on this principle, we propose Gaze-DETR, a bio-inspired detector that learns an internal priority map before localization. First, a priority head predicts a normalized priority map from image features. Second, Residual Priority-Guided Feature Modulation (RPFM) enhances high-priority responses while retaining multi-scale features. Finally, Priority-Guided Anchor Query Injection (PAQI) converts high-priority locations into decoder anchor queries. We train the priority head using three supervision schemes: box-derived Gaussian maps; real-gaze maps constructed from fixation-density maps; and transferred pseudo-gaze maps learned from gaze--box relations in paired annotations and applied to Anti-UAV410 training boxes. To support the latter two schemes, we construct TIR-UAV120-Gaze with paired detection and task-driven eye-tracking annotations. On TIR-UAV120-Gaze, Gaze-DETR achieves 85.76 mAP$_{50}$ and 88.77 F1 with box-derived supervision, and 86.18 mAP$_{50}$ and 89.00 F1 with real-gaze supervision. On Anti-UAV410, it achieves 87.06 mAP$_{50}$ and 90.90 F1 with box-derived supervision, and 87.08 mAP$_{50}$ and 90.43 F1 with transferred pseudo-gaze supervision. These results show that explicit spatial-priority learning provides pre-localization guidance complementary to bounding-box supervision across annotation settings and costs.
CommentsCode: https://github.com/nliu-25/Gaze-DETR-Top-Down-Guidance-Through-Priority-Maps-for-Infrared-Weak-Small-UAV-Detection-with-DETR
