Camouflaged object detection (COD) aims to segment camouflaged objects hiding in the environment, which is challenging due to the similar appearance of camouflaged objects and their surroundings. Research in biology suggests that depth can provide useful object localization cues for camouflaged object discovery, as all the animals have 3D perception ability. However, the depth information has not been exploited for camouflaged object detection. To explore the contribution of depth for camouflage detection, we present a depth-guided camouflaged object detection network with pre-computed depth maps from existing monocular depth estimation methods. Due to the domain gap between the depth estimation dataset and our camouflaged object detection dataset, the generated depth may not be accurate enough to be directly used in our framework. We then introduce a depth quality assessment module to evaluate the quality of depth based on the model prediction from both RGB COD branch and RGB-D COD branch. During training, only high-quality depth is used to update the modal interaction module for multi-modal learning. During testing, our depth quality assessment module can effectively determine the contribution of depth and select the RGB branch or RGB-D branch for camouflage prediction. Extensive experiments on various camouflaged object detection datasets prove the effectiveness of our solution in exploring the depth information for camouflaged object detection. Our code and data is publicly available at: \url{https://github.com/JingZhang617/RGBD-COD}.
翻译:隐蔽物体探测(COD)的目的是将隐藏在环境中的伪装物体进行分解,由于伪装物体及其周围环境的相似外观,这具有挑战性。生物学研究表明,深度可以为伪装物体的发现提供有用的物体定位线索,因为所有动物都有3D感知能力。但是,深度信息尚未用于隐蔽物体探测。为了探索隐蔽物体探测的深度贡献,我们提出了一个深层制导伪装物体探测网,根据现有的单体深度估计方法,预先绘制深度地图。由于深度估计数据集与我们伪装物体探测数据集之间的地域差距,产生的深度可能不够准确,无法直接用于我们的框架。我们随后引入一个深度质量评估模块,以根据来自RGB COD分支和RGB-D COD分支的模型预测来评估深度质量。在培训期间,只有高质量的深度用于更新多模式学习模式互动模块。在测试期间,我们的深度评估模块能够有效地确定深度贡献,并选择RGB 目标分支或RGB-D的伪装用途探测数据集,我们用于进行公开的探险检测。