高分辨率水下伪装目标检测：GBU-UCOD数据集和拓扑感知与频率解耦网络
High-Resolution Underwater Camouflaged Object Detection: GBU-UCOD Dataset and Topology-Aware and Frequency-Decoupled Networks
AI总结：
本文提出DeepTopo-Net框架和GBU-UCOD数据集，通过拓扑感知与频率解耦技术提升水下伪装目标检测性能。
摘要：
水下伪装目标检测（UCOD）是一个具有挑战性的任务，因为目标与背景在不同海洋深度下具有极强的视觉相似性。现有方法往往难以处理深海中细长生物的拓扑碎片化以及透明生物的细微特征提取。在本文中，我们提出了DeepTopo-Net，一种集成了拓扑感知建模与频率解耦感知的新型框架。为了解决物理退化问题，我们设计了水条件自适应感知器（WCAP），它利用黎曼度量张量动态变形卷积采样场。此外，开发了深渊拓扑细化模块（ATRM），通过骨骼先验保持细长目标的结构连通性。具体而言，我们首先引入GBU-UCOD，第一个高分辨率（2K）的海洋垂直分层专用基准数据集，填补了深海和深渊区的数据缺口。在MAS3K、RMAS和我们提出GBU-UCOD数据集上的大量实验表明，DeepTopo-Net在保持复杂水下模式形态完整性方面取得了最先进的性能。数据集和代码将在https://github.com/Wuwenji18/GBU-UCOD上发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
