将水下成像物理规律融入网络训练，提升水下显著目标检测精度
WaterFlow: Explicit Physics-Prior Rectified Flow for Underwater Saliency Mask Generation
- 引入水下成像物理先验作为显式约束，指导模型学习
- 在USOD10K上S_m指标提升0.072，验证方法有效性
- 适合需要高精度水下目标识别的科研与应用团队
水下显著目标检测（USOD）面临图像质量退化和域差异等挑战。现有方法通常忽略水下成像的物理原理，或将退化现象简单视为需消除的干扰，未能充分利用其蕴含的有效信息。本文提出WaterFlow，一种基于修正流（rectified flow）的水下显著目标检测框架，创新性地将水下物理成像信息作为显式先验直接嵌入网络训练过程，并引入时序维度建模，显著增强模型对显著目标的识别能力。在USOD10K数据集上，WaterFlow实现S_m指标提升0.072，充分证明了方法的有效性与优越性。
原文摘要 · Abstract (English)
Underwater Salient Object Detection (USOD) faces significant challenges, including underwater image quality degradation and domain gaps. Existing methods tend to ignore the physical principles of underwater imaging or simply treat degradation phenomena in underwater images as interference factors that must be eliminated, failing to fully exploit the valuable information they contain. We propose WaterFlow, a rectified flow-based framework for underwater salient object detection that innovatively incorporates underwater physical imaging information as explicit priors directly into the network training process and introduces temporal dimension modeling, significantly enhancing the model's capability for salient object identification. On the USOD10K dataset, WaterFlow achieves a 0.072 gain in S_m, demonstrating the effectiveness and superiority of our method. https://github.com/Theo-polis/WaterFlow.
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