无需人工标注，自监督学习提升足球机器人识球能力
Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots
- 用预训练模型生成伪标签，通过颜色化等任务自学习特征
- 在1万张室外比赛图像上验证，准确率、F1和IoU均优于基线
- 适合快速适配新场景的足球机器人视觉系统
自主类人足球机器人在动态复杂的室外赛场（如RoboCup）中，稳健精准的球体检测至关重要。传统监督方法依赖大量人工标注，成本高且耗时。为此，本文提出一种自监督学习框架，用于领域自适应特征提取，以增强球体检测性能。该方法利用通用预训练模型生成伪标签，并在一系列自监督预训练任务（包括颜色化、边缘检测、三元组损失）中学习鲁棒视觉特征，无需人工标注。此外，引入模型无关元学习（MAML）策略，实现仅需少量标注即可快速适应新部署场景。本文构建了一个包含10,000张室外RoboCup SPL比赛标注图像的新数据集，用于方法验证并开源。实验表明，所提方法在准确率、F1分数和交并比（IoU）上均优于基线模型，且收敛更快。
原文摘要 · Abstract (English)
Robust and accurate ball detection is a critical component for autonomous humanoid soccer robots, particularly in dynamic and challenging environments such as RoboCup outdoor fields. However, traditional supervised approaches require extensive manual annotation, which is costly and time-intensive. To overcome this problem, we present a self-supervised learning framework for domain-adaptive feature extraction to enhance ball detection performance. The proposed approach leverages a general-purpose pretrained model to generate pseudo-labels, which are then used in a suite of self-supervised pretext tasks -- including colorization, edge detection, and triplet loss -- to learn robust visual features without relying on manual annotations. Additionally, a model-agnostic meta-learning (MAML) strategy is incorporated to ensure rapid adaptation to new deployment scenarios with minimal supervision. A new dataset comprising 10,000 labeled images from outdoor RoboCup SPL matches is introduced, used to validate the method, and made available to the community. Experimental results demonstrate that the proposed pipeline outperforms baseline models in terms of accuracy, F1 score, and IoU, while also exhibiting faster convergence.
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