Abstract：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.
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2024, Vol. 37 
