Depth context aggregation network for camouflaged object detection

Camouflaged object detection (COD) intends to find concealed objects hidden in the surroundings. COD is challenging for it has to discriminate the minor difference between foreground and background. In most existing methods, convolutional neural network (CNN)-based approaches are proposed to overcome this challenge. However, they have limitations in extracting semantic features of input images and learning global contexts. This study presents a novel Transformer and CNN-based Depth Context Aggregation network (call DCA-Net) for concealed object detection and segmentation. This network uses Swin Transformer as backbone to extract globalized semantic features. Dilated Reception (DR) module is designed to connect the encoder and decoder. A hybrid loss function is used for optimizing the model. In particular, to supplement the intersection over union (IOU) loss, Complementary (C) loss is introduced. By analyzing different metrics, comprehensive comparisons with previous methods are conducted on four public datasets, such as CAMO, CHAMELEON, COD10K, and NC4K. Experimental analysis proves that the proposed DCA-Net achieves state-of-the-art performance.