Underwater Camouflaged Object Detection (UCOD) is a challenging task due to the extreme visual similarity between targets and backgrounds across varying marine depths. Existing methods often struggle with topological fragmentation of slender creatures in the deep sea and the subtle feature extraction of transparent organisms. In this paper, we propose DeepTopo-Net, a novel framework that integrates topology-aware modeling with frequency-decoupled perception. To address physical degradation, we design the Water-Conditioned Adaptive Perceptor (WCAP), which employs Riemannian metric tensors to dynamically deform convolutional sampling fields. Furthermore, the Abyssal-Topology Refinement Module (ATRM) is developed to maintain the structural connectivity of spindly targets through skeletal priors. Specifically, we first introduce GBU-UCOD, the first high-resolution (2K) benchmark tailored for marine vertical zonation, filling the data gap for hadal and abyssal zones. Extensive experiments on MAS3K, RMAS, and our proposed GBU-UCOD datasets demonstrate that DeepTopo-Net achieves state-of-the-art performance, particularly in preserving the morphological integrity of complex underwater patterns. The datasets and codes will be released at https://github.com/Wuwenji18/GBU-UCOD.

翻译：水下伪装目标检测（UCOD）是一项极具挑战性的任务，原因在于不同海洋深度下目标与背景之间存在极高的视觉相似性。现有方法通常难以处理深海细长生物的拓扑结构断裂问题，以及透明生物的细微特征提取。本文提出DeepTopo-Net，一种将拓扑感知建模与频率解耦感知相结合的新型框架。为应对物理退化问题，我们设计了水环境自适应感知器（WCAP），该模块利用黎曼度量张量动态变形卷积采样场。此外，开发了深渊拓扑优化模块（ATRM），通过骨架先验知识保持细长目标的结构连通性。具体而言，我们首先引入了GBU-UCOD，这是首个针对海洋垂直分带定制的高分辨率（2K）基准数据集，填补了超深渊带与深渊带的数据空白。在MAS3K、RMAS以及我们提出的GBU-UCOD数据集上进行的大量实验表明，DeepTopo-Net实现了最先进的性能，尤其在保持复杂水下图案形态完整性方面表现突出。数据集与代码将在https://github.com/Wuwenji18/GBU-UCOD发布。