发表机构
QUT Centre for Robotics, Queensland University of Technology（昆士兰科技大学机器人中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究水下目标检测中域转移对检测性能和标注质量的影响，引入通过外观等特征分配域标签的框架，系统研究域因素作用，发现显著差异，可用于指导数据收集等工作，提升检测效果及评估检测器鲁棒性。
AI 中文摘要
水下目标检测受域转移影响很大，不同位置、栖息地和部署条件下性能差异显著。现有评估方法存在缺陷，我们引入一个通过外观、场景构成和采集几何特征来分配域标签的框架。利用此框架，首次系统研究域因素对水下目标检测数据集中人工标注质量和深度学习检测器性能的影响，发现显著的域相关差异。纳入有物理意义的域标签后，可对域转移进行表征、测量、基准测试及应对，用于指导数据收集与标注、设计更具信息性的基准测试以及评估检测器在不同水下环境的鲁棒性。
英文摘要
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.
