提出可量化水下场景差异的标注框架，助力精准检测性能评估
Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection
- 基于图像、场景与采集特征定义水下领域，实现物理意义明确的分组
- 实证发现不同领域间存在系统性性能差异，揭示隐藏的模型失败模式
- 适用于水下目标检测模型的领域特异性评估与故障分析
域偏移问题在水下环境中尤为突出，训练数据与部署数据分布不一致会显著降低模型性能。现有基准通过合成风格迁移模拟域变化，但无法捕捉能见度、光照、场景构成和采集条件等真实场景因素，限制了对实际影响的分析。本文提出一种基于可测量图像、场景和采集特征的标注框架，用于定义水下域。相比以往方法，该框架能捕捉具有物理意义的因素，实现语义一致的图像分组，并支持针对特定域的检测性能评估，包括失败分析。我们在公开数据集上验证了该框架，发现域因素间存在系统性差异，并揭示了被忽视的模型失效模式。
原文摘要 · Abstract (English)
Domain shift, where deviations between training and deployment data distributions degrade model performance, is a key challenge in underwater environments. Existing benchmarks testing performance for underwater domain shift simulate variability through synthetic style transfer. This fails to capture intrinsic scene factors such as visibility, illumination, scene composition, or acquisition factors, limiting analysis of real-world effects. We propose a labeling framework that defines underwater domains using measurable image, scene, and acquisition characteristics. Unlike prior benchmarks, it captures physically meaningful factors, enabling semantically consistent image grouping and supporting domain-specific evaluation of detection performance including failure analysis. We validate this on public datasets, showing systematic variations across domain factors and revealing hidden failure modes.
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