为移动机器人设计的实时羽毛球检测框架，支持动态视角下的精准识别。
One-Shot Badminton Shuttle Detection for Mobile Robots
- 基于半自动标注流程构建20,510帧羽毛球数据集，覆盖多种复杂背景。
- 在相似环境下实现0.86的F1分数，未知环境仍达0.70，表现稳定。
- 专为移动机器人动态视角设计，适合追踪与轨迹估计等下游任务。
本文提出一种适用于非固定机器人的鲁棒单次羽毛球球头检测框架。针对缺乏第一人称视角羽毛球检测数据集的问题，我们构建了一个包含20,510帧半自动标注图像的数据集，覆盖11种不同背景，且在多样化的室内外环境中采集，并将每帧按难度分为三个等级。为此开发了一种新型半自动标注流程，可高效处理静态摄像头拍摄的视频。针对下游应用需求，设计了专用评估指标，并微调YOLOv8网络以实现实时检测，在与训练环境相似的测试场景中取得0.86的F1分数，而在完全未见过的环境中也达到0.70。分析表明，检测性能高度依赖于羽毛球尺寸及背景纹理复杂度。定性实验验证了其在带运动相机的机器人上的适用性。与以往使用固定摄像头的研究不同，本检测器专为移动机器人提供的第一人称动态视图设计，为后续追踪、轨迹估计及系统（重）初始化等任务提供了基础支撑。
原文摘要 · Abstract (English)
This paper presents a robust one-shot badminton shuttlecock detection framework for non-stationary robots. To address the lack of egocentric shuttlecock detection datasets, we introduce a dataset of 20,510 semi-automatically annotated frames captured across 11 distinct backgrounds in diverse indoor and outdoor environments, and categorize each frame into one of three difficulty levels. For labeling, we present a novel semi-automatic annotation pipeline, that enables efficient labeling from stationary camera footage. We propose a metric suited to our downstream use case and fine-tune a YOLOv8 network optimized for real-time shuttlecock detection, achieving an F1-score of 0.86 under our metric in test environments similar to training, and 0.70 in entirely unseen environments. Our analysis reveals that detection performance is critically dependent on shuttlecock size and background texture complexity. Qualitative experiments confirm their applicability to robots with moving cameras. Unlike prior work with stationary camera setups, our detector is specifically designed for the egocentric, dynamic viewpoints of mobile robots, providing a foundational building block for downstream tasks, including tracking, trajectory estimation, and system (re)-initialization.
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