Decode after filtering: a network for camouflage object segmentation

As a derivative task of object segmentation, camouflage object segmentation has the difficulties of redundant complex information and anti-detection objects. Most object segmentation algorithms are dedicated to improving the structure of the feature extraction and fusion modules, but the processing of complex redundant information is not sufficient, which makes them unable to segment the camouflage objects well. Aiming at the data characteristics of the camouflage object, we propose a novel structure of fully convolutional network called DAFNet, which mainly consists of feature filter module (FFM). FFM is formed by multi-path dilated convolution through multiplication, which “filters” the redundant features flowing in the network to improve the network’s segmentation performance on camouflage objects. We also design an attention module based on Gaussian convolution called Gaussian attention module (GAM), which is used to refine the rough predicted map to further improve the output quality. Experiments on existing camouflage object datasets show that the DAFNet can achieve state-of-the-art performance on camouflage object segmentation.