Camouflaged Object Detection with Feature Grafting and Distractor Aware
Yuxuan Song Xinyue Li Lin Qi
The task of Camouflaged Object Detection (COD) aims to accurately segment
camouflaged objects that integrated into the environment, which is more
challenging than ordinary detection as the texture between the target and
background is visually indistinguishable. In this paper, we proposed a novel
Feature Grafting and Distractor Aware network (FDNet) to handle the COD task.
Specifically, we use CNN and Transformer to encode multi-scale images in
parallel. In order to better explore the advantages of the two encoders, we
design a cross-attention-based Feature Grafting Module to graft features
extracted from Transformer branch into CNN branch, after which the features are
aggregated in the Feature Fusion Module. A Distractor Aware Module is designed
to explicitly model the two possible distractors in the COD task to refine the
coarse camouflage map. We also proposed the largest artificial camouflaged
object dataset which contains 2000 images with annotations, named ACOD2K. We
conducted extensive experiments on four widely used benchmark datasets and the
ACOD2K dataset. The results show that our method significantly outperforms
other state-of-the-art methods. The code and the ACOD2K will be available at
https://github.com/syxvision/FDNet.