伪装物体检测旨在分离视觉上高度融入周围环境的物体,但是物体前景与背景存在大量相似干扰,导致分割过程中易于出现明显错误.针对上述问题,文中提出基于语义重建的动态监督伪装物体检测网络(Dynamic Su-pervised Camouflaged Object Detection Network with Semantic Reconstruction,DSSRNet),通过重建特征图的空间语义和引入置信度指导网络训练,实现对伪装物体的准确分割.首先,提出空间语义低秩重建机制,精细感知不同尺度下伪装物体具有区分性的语义特征.然后,生成预测置信度图,对分割过程进行动态监督,减少网络因过于自信造成的假阳性和假阴性判断.最后,提出模糊感知损失函数,对网络施加强约束,改善预测时产生的图像模糊问题.在3个具有挑战性的基准数据集上的实验表明,DSSRNet可较好地排除相似信息干扰,取得精准的分割效果.
Dynamic Supervised Camouflaged Object Detection with Semantic Reconstruction
Camouflaged object detection(COD)aims to segment target objects that are visually highly integrated into their surrounding environments.However,a large number of similar interferences between the foreground and background of the object lead to significant segmentation errors in the process.To address this issue,dynamic supervised camouflaged object detection network with semantic reconstruction(DSSRNet)is proposed to achieve accurate segmentation of camouflaged objects by reconstructing the spatial semantics of the feature map and introducing confidence to guide network training.Firstly,a spatial semantic low-rank reconstruction mechanism is proposed to effectively perceive distinguishable semantic features of camouflaged objects at different scales.Secondly,the COD network is dynamically supervised by generating confidence prediction maps to minimize false positive and false negative judgments due to the overconfidence in the network.Finally,the blurred awareness loss function is employed to reduce the ambiguity of the prediction.Experiments on CAMO-Test,COD10K-Test and NC4K datasets demonstrate that DSSRNet provides better exclusion of interference and achieves more accurate segmentation results.
万方数据
维普
