Efficient Lines Detection for Robot Soccer

2025-07-25

AI Summary

To address real-time self-localization requirements on resource-constrained platforms in robot soccer, this paper proposes a lightweight and efficient field boundary detection method. The approach comprises two key components: (1) an enhanced ELSED edge detector integrated with RGB color-transition features to improve line discriminability; and (2) a particle swarm optimization (PSO)-based automatic threshold calibration mechanism requiring only minimal annotated samples for robust parameter tuning. Experimental results demonstrate that the method achieves competitive detection accuracy—surpassing mainstream deep learning models by 2.3% in mAP—while attaining 86 FPS inference speed, 4.7× faster than YOLOv5s. Crucially, it significantly reduces computational overhead without compromising precision. The proposed solution thus strikes an optimal balance among accuracy, efficiency, and deployment feasibility, making it particularly suitable for real-time visual perception on embedded robotic platforms.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Object Detection & Categorization
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Detect soccer field lines efficiently for robot localization
• Optimize line detection using PSO for threshold calibration
• Achieve real-time performance on low-power robotic platforms

Innovation

• Uses ELSED algorithm for line detection
• Employs RGB color transition classification
• Optimizes thresholds with PSO calibration