浑浊水下图像中分割噪声与不确定性的多标注者研究
A Multi-Annotator Study of Segmentation Noise and Uncertainty in Turbid Underwater Images
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- Aalborg University（奥尔堡大学）
- Pioneer Centre for Artificial Intelligence（先锋人工智能中心）
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中文总结 AI 辅助
本研究开展首个真实水下场景分割的多标注者系统性研究，发现水下数据集存在通用视觉任务的标注挑战且浑浊度引入额外误差，探索了提升标注质量的方法，相关数据将公开。
中文摘要 AI 辅助
标签不确定性与标注者分歧是计算机视觉领域的常见挑战，但相关研究多局限于医学领域或通用图像识别数据集。水下数据集因需要领域专业知识、可见度条件退化，以及在难以到达的环境中建立可靠真值的固有困难，尤其易受这些问题影响。尽管存在这些挑战，水下图像的标注不确定性仍在很大程度上未被探索。在本研究中，我们开展了首个针对真实水下场景分割的系统性多标注者研究，共有超过100名参与者，且涵盖不同的、受控的浑浊度水平。我们表明，水下数据集面临与其他视觉任务相同的诸多标注挑战，而浑浊度会引入额外的系统性误差。我们进一步探究了导致标签噪声的主要因素，并探索了提高浑浊水下环境中标注质量的方法，包括特权信息、个体努力及标注者集成。本研究中收集的所有（元）数据将在项目页面提供：this https URL
英文摘要
Label uncertainty and annotator disagreement are common challenges in the field of computer vision, yet their study has largely been confined to the medical domain or to generic image-recognition datasets. Underwater datasets are particularly susceptible to these issues due to the need for domain expertise, degraded visibility conditions, and the inherent difficulty of establishing reliable ground truth in inaccessible environments. Despite these challenges, annotation uncertainty in underwater imagery remains largely unexplored. In this work, we present the first systematic multi-annotator study of segmentation in real underwater scenes, with over 100 participants, and across varying, controlled levels of turbidity. We show that underwater datasets face many of the same annotation challenges as other vision tasks, while turbidity introduces additional systematic errors. We further investigate the main factors driving label noise and explore ways to improve annotation quality in turbid underwater environments, including privileged information, individual effort and annotator ensembles. All (meta-) data collected in this study will be available on the project page: https://vap.aau.dk/tubcertainty
