  Camouflaged object detection (COD), which aims to identify the objects that
conceal themselves into the surroundings, has recently drawn increasing
research efforts in the field of computer vision. In practice, the success of
deep learning based COD is mainly determined by two key factors, including (i)
A significantly large receptive field, which provides rich context information,
and (ii) An effective fusion strategy, which aggregates the rich multi-level
features for accurate COD. Motivated by these observations, in this paper, we
propose a novel deep learning based COD approach, which integrates the large
receptive field and effective feature fusion into a unified framework.
Specifically, we first extract multi-level features from a backbone network.
The resulting features are then fed to the proposed dual-branch mixture
convolution modules, each of which utilizes multiple asymmetric convolutional
layers and two dilated convolutional layers to extract rich context features
from a large receptive field. Finally, we fuse the features using
specially-designed multi-level interactive fusion modules, each of which
employs an attention mechanism along with feature interaction for effective
feature fusion. Our method detects camouflaged objects with an effective fusion
strategy, which aggregates the rich context information from a large receptive
field. All of these designs meet the requirements of COD well, allowing the
accurate detection of camouflaged objects. Extensive experiments on widely-used
benchmark datasets demonstrate that our method is capable of accurately
detecting camouflaged objects and outperforms the state-of-the-art methods.
