提出SLENet模型，提升水下伪装目标检测精度
SLENet: A Guidance-Enhanced Network for Underwater Camouflaged Object Detection
- 引入定位引导分支和伽马非对称增强模块
- 在DeepCamo等数据集上优于现有方法
- 适合海洋生态监测与水下视觉研究者
水下伪装目标检测（UCOD）旨在识别与水下环境融为一体的物体，对海洋生态研究至关重要。但光学畸变、水体浑浊及生物特征复杂性严重阻碍了准确识别。为此，我们提出了UCOD任务，并构建了DeepCamo基准数据集。在此基础上，提出语义定位与增强网络（SLENet），通过引入伽马非对称增强（GAE）模块和定位引导分支（LGB），增强多尺度特征表示，并生成富含全局语义信息的定位图，指导多尺度监督解码器（MSSD）生成更精确预测。在DeepCamo及三个基准COD数据集上的实验表明，SLENet显著优于当前最先进方法，展现出在更广泛伪装目标检测任务中的强泛化能力。
原文摘要 · Abstract (English)
Underwater Camouflaged Object Detection (UCOD) aims to identify objects that blend seamlessly into underwater environments. This task is critically important to marine ecology. However, it remains largely underexplored and accurate identification is severely hindered by optical distortions, water turbidity, and the complex traits of marine organisms. To address these challenges, we introduce the UCOD task and present DeepCamo, a benchmark dataset designed for this domain. We also propose Semantic Localization and Enhancement Network (SLENet), a novel framework for UCOD. We first benchmark state-of-the-art COD models on DeepCamo to reveal key issues, upon which SLENet is built. In particular, we incorporate Gamma-Asymmetric Enhancement (GAE) module and a Localization Guidance Branch (LGB) to enhance multi-scale feature representation while generating a location map enriched with global semantic information. This map guides the Multi-Scale Supervised Decoder (MSSD) to produce more accurate predictions. Experiments on our DeepCamo dataset and three benchmark COD datasets confirm SLENet's superior performance over SOTA methods, and underscore its high generality for the broader COD task.
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