Abstract
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.
Abstract (translated)
鲁棒且准确的球检测是自主人形足球机器人在动态和具有挑战性的环境中（如RoboCup室外场地）的关键组成部分。然而，传统的监督学习方法需要大量的手动标注工作，这既耗时又昂贵。为了解决这个问题，我们提出了一种基于领域自适应特征提取的自我监督学习框架，以提高球检测性能。该方法利用一个通用的预训练模型生成伪标签，并通过一系列自我监督任务（包括色彩恢复、边缘检测和三元组损失）来学习稳健的视觉特征，而无需依赖手动标注。 此外，我们还整合了一种与模型无关的元学习策略(MAML)，以确保在新的部署场景中快速适应并减少对监督的需求。同时，我们引入了一个包含10,000张标签图像的新数据集，这些图像是从户外RoboCup SPL比赛中收集而来，并将用于验证该方法的有效性并向社区开放。 实验结果表明，所提出的框架在准确率、F1值和IoU指标上均优于基准模型，并且收敛速度更快。
URL
https://arxiv.org/abs/2506.16821
