首次系统分析水下检测中域因素影响，揭示环境差异对模型与标注质量的显著作用。
Why Domain Matters: Domain-Aware Benchmarking of Underwater Object Detection and Annotation Quality
- 按外观、场景、成像几何划分水下图像域标签，实现可量化的域表征
- 发现不同水域环境下标注质量与检测性能差异巨大，传统平均指标掩盖问题
- 为数据采集、评测设计和模型鲁棒性评估提供可操作的域感知框架
水下目标检测受域偏移强烈影响，性能在不同地点、生境和部署条件下差异显著。然而，现有评估多采用聚合指标，掩盖了特定环境中的失败；现有域泛化基准常依赖合成变化，无法反映真实场景。本文提出一个基于外观、场景构成和成像几何的水下图像域表征框架，首次系统研究域因素对人工标注质量和深度学习检测器性能的影响，揭示显著的域依赖性差异。通过引入具有物理意义的域标签，域偏移得以被描述、度量、基准化并用于指导实践。该方法可指导数据收集与标注，设计更有效的评测基准，并评估模型在多样水下环境中的鲁棒性。
原文摘要 · Abstract (English)
Underwater object detection is strongly affected by domain shift, where performance can vary significantly across different locations, habitats, and deployment conditions. However, detector performance is typically evaluated using aggregate metrics that hide failures in specific environments, while existing domain generalization benchmarks often rely on synthetic variations that do not reflect real-world conditions. We introduce a framework that characterizes underwater images by appearance, scene composition, and acquisition geometry to assign domain labels. Using this framework, we perform the first systematic study of how domain factors influence both human annotation quality in underwater object detection datasets and deep learning-based detector performance, revealing substantial domain-dependent discrepancies. By incorporating physically meaningful domain labels, domain shift becomes something we can characterize, measure, benchmark, and act on. We highlight how this can be used to guide data collection and annotation, design more informative benchmarks, and assess detector robustness across diverse underwater environments.
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