针对深海伪装目标检测难题，提出高分辨率数据集与新型感知网络。
High-Resolution Underwater Camouflaged Object Detection: GBU-UCOD Dataset and Topology-Aware and Frequency-Decoupled Networks
- 融合拓扑感知与频域解耦的新型检测框架
- 在2K分辨率下显著提升复杂水下形态保持能力
- 适合海洋探测、生物识别等深海视觉任务研究者
由于不同海洋深度下目标与背景视觉相似度极高，水下伪装目标检测（UCOD）极具挑战。现有方法难以处理深海细长生物的拓扑碎片化及透明生物的微弱特征提取问题。本文提出DeepTopo-Net框架，结合拓扑感知建模与频域解耦感知。为应对物理退化，设计了基于黎曼度量张量的水下自适应感知器（WCAP），动态调整卷积采样区域；并提出深渊拓扑优化模块（ATRM），通过骨骼先验保持细长目标结构连通性。首次构建了面向海洋垂直分带的高分辨率（2K）基准数据集GBU-UCOD，填补了海沟与深渊区数据空白。在MAS3K、RMAS及GBU-UCOD数据集上的实验表明，DeepTopo-Net在保持复杂水下形态完整性方面达到当前最优性能。代码与数据集将开源于https://github.com/Wuwenji18/GBU-UCOD。
原文摘要 · Abstract (English)
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.
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