轻量级算法高效检测机器人足球场线，实时性好适合低功耗设备
Efficient Lines Detection for Robot Soccer
- 用改进的ELSED算法结合颜色变化分类识别球场线
- 仅需少量标注样本，通过粒子群优化调参，精度媲美深度学习模型
- 速度快、资源消耗低，适合嵌入式机器人实时定位
自定位在机器人足球中至关重要，准确检测视觉场中的线条和边界对可靠姿态估计尤为关键。本文提出一种轻量高效的方法，基于ELSED算法并引入分类步骤，通过分析RGB颜色变化来识别属于球场的线条。我们设计了一套基于粒子群优化（PSO）的阈值校准流程，仅需少量标注样本即可优化检测性能。该方法在精度上接近当前最先进的深度学习模型，同时具备更高处理速度，非常适合部署于低功耗机器人平台的实时应用。
原文摘要 · Abstract (English)
Self-localization is essential in robot soccer, where accurate detection of visual field features, such as lines and boundaries, is critical for reliable pose estimation. This paper presents a lightweight and efficient method for detecting soccer field lines using the ELSED algorithm, extended with a classification step that analyzes RGB color transitions to identify lines belonging to the field. We introduce a pipeline based on Particle Swarm Optimization (PSO) for threshold calibration to optimize detection performance, requiring only a small number of annotated samples. Our approach achieves accuracy comparable to a state-of-the-art deep learning model while offering higher processing speed, making it well-suited for real-time applications on low-power robotic platforms.
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