# Efficient Lines Detection for Robot Soccer

arXiv: https://arxiv.org/abs/2507.19469 · HTML: https://mlsanity.com/paper/2507.19469/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 2 of 2)

- [ELSED: Enhanced Line SEgment Drawing](https://mlsanity.com/paper/2108.03144/) (2021)
- [A Monocular Vision System for Playing Soccer in Low Color Information Environments](https://mlsanity.com/paper/1809.11078/) (2018)

## Abstract (arXiv)

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.