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Camouflaged object detection (COD), which aims to identify the objects thatconceal themselves into the surroundings, has recently drawn increasingresearch efforts in the field of computer vision. In practice, the success ofdeep 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-levelfeatures for accurate COD. Motivated by these observations, in this paper, wepropose a novel deep learning based COD approach, which integrates the largereceptive 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 mixtureconvolution modules, each of which utilizes multiple asymmetric convolutionallayers and two dilated convolutional layers to extract rich context featuresfrom a large receptive field. Finally, we fuse the features usingspecially-designed multi-level interactive fusion modules, each of whichemploys an attention mechanism along with feature interaction for effectivefeature fusion. Our method detects camouflaged objects with an effective fusionstrategy, which aggregates the rich context information from a large receptivefield. All of these designs meet the requirements of COD well, allowing theaccurate detection of camouflaged objects. Extensive experiments on widely-usedbenchmark datasets demonstrate that our method is capable of accuratelydetecting camouflaged objects and outperforms the state-of-the-art methods.

Accurate Camouflaged Object Detection via Mixture Convolution and Interactive Fusion