Abstract: This paper proposes a Prediction Prior and Uncertainty Refinement Network (PURNet) for camouflaged object detection (COD) to address false positives, missed detections, and inaccurate boundary localization in existing methods. The proposed method adopts a Pyramid Vision Transformer (PVT) as the backbone to extract multi-scale features with rich semantic information. Based on these features, a Dual Prior-guided Attention Module (DPAM) is designed. The module uses the prediction result of the current stage as a prediction prior and adaptively enhances features from both spatial and channel perspectives. Specifically, the spatial prior guides the features to focus on potential object regions and suppress irrelevant background responses. The channel prior dynamically generates channel attention parameters according to the current prediction state. It reweights target-related semantic features to enhance the feature representation of camouflaged objects. To further evaluate the reliability of prediction results, an Uncertainty Generation Module (UGM) is introduced. The module performs probabilistic modeling and reparameterization sampling to generate multiple prediction samples. It estimates uncertainty from the variance of these samples. Furthermore, a Group Uncertainty-Guided Residual Refinement (GUGR) Module is proposed. The module adopts different levels of uncertainty-guided strategies to progressively integrate uncertainty information with features and refine unreliable regions. It enables the network to adaptively adjust prediction results according to their reliability. Experiments are conducted on three public COD datasets, namely CAMO-Test, COD10K-Test, and NC4K. Four metrics are used for comprehensive evaluation, including Structure Measure, Enhanced Measure, Weighted F-measure, and Mean Absolute Error. On CAMO-Test, PURNet achieves 0.856, 0.920, 0.804, and 0.050, respectively. On COD10K-Test, the corresponding results are 0.839, 0.914, 0.730, and 0.029, respectively. On NC4K, the corresponding results are 0.870, 0.926, 0.812, and 0.037, respectively. Complexity and inference efficiency experiments show that PURNet requires 10.77 GFLOPs and 25.82 M parameters and achieves 78.6 FPS. Overall, the experimental results demonstrate that PURNet effectively integrates prediction priors and uncertainty information and achieves more accurate object localization and segmentation in complex backgrounds.

摘要： 针对现有伪装目标检测（COD）方法易产生误检、漏检及边界定位不准确等问题，提出一种基于预测先验与不确定性细化的伪装目标检测方法（PURNet）。该方法以金字塔视觉Transformer（PVT）作为骨干网络，获得具有丰富语义信息的多尺度特征。在此基础上，设计双路先验引导注意力模块（DPAM），将当前阶段的预测结果作为预测先验，并从空间和通道两个维度对特征进行自适应增强。其中，空间先验引导特征关注潜在目标区域，抑制背景区域的无关响应；通道先验根据当前预测状态动态生成通道注意力参数，对目标相关语义特征进行重新加权，以增强模型对目标的特征表达能力。为进一步衡量预测结果的可靠性，引入不确定性生成模块（UGM），通过概率分布建模和重参数化采样获得多组预测样本，并利用样本的方差估计不确定性。进一步提出组不确定性引导残差细化模块（GUGR），采用不同程度的不确定性引导策略，逐步融合不确定性信息与特征，同时对不可靠区域进行渐进式细化，使模型能够根据预测可靠程度自适应调整预测结果。为验证所提方法的有效性，在CAMO-Test、COD10K-Test和NC4K三个公开伪装目标检测数据集上开展实验，并从结构度量 、增强对准度量 、加权 以及平均绝对误差MAE四个指标进行综合评价。在CAMO-Test上，模型取得0.856的 、0.920的 、0.804的 以及0.050的MAE；在COD10K-Test上分别取得0.839、0.914、0.730和0.029；在NC4K上分别为0.870、0.926、0.812和0.037。模型复杂度与推理效率实验表明， PURNet的计算量为10.77 GFLOPs，参数量为25.82 M，推理速度达到78.6 FPS。综合实验结果表明，PURNet能够有效融合预测先验与不确定性信息，在复杂背景下实现更准确的目标定位与分割。