One-Shot Badminton Shuttle Detection for Mobile Robots

2026-03-04

AI Summary

This work addresses the lack of efficient badminton shuttlecock detection methods for mobile robots operating under dynamic egocentric viewpoints by proposing the first single-stage detection framework tailored to this scenario. The authors construct the first large-scale, multi-scenario badminton dataset with difficulty-level annotations and employ a semi-automatic labeling pipeline to enhance data quality. Building upon an optimized YOLOv8 architecture and introducing custom evaluation metrics aligned with downstream tasks, the framework achieves end-to-end real-time detection. Experimental results demonstrate strong performance, with an F1-score of 0.86 in similar environments and a robust 0.70 in completely unseen settings, confirming the model’s robustness and generalization capability under dynamic viewpoints.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• one-shot detection
• badminton shuttlecock
• mobile robots
• egocentric vision
• dynamic viewpoint

Innovation

• one-shot detection
• egocentric vision
• mobile robot perception
• semi-automatic annotation
• YOLOv8 optimization