Efficient Lines Detection for Robot Soccer

João G. Melo; João P. Mafaldo; Edna Barros

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arXiv · 2507.19469

Efficient Lines Detection for Robot Soccer

João G. Melo·João P. Mafaldo·Edna Barros

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Abstract

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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João G. Melo, João P. Mafaldo, Edna Barros. 2025-07-25. Efficient Lines Detection for Robot Soccer. https://arxiv.org/abs/2507.19469

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