GLCONet: Learning Multisource Perception Representation for Camouflaged Object Detection

Recently, the biological perception has been a powerful tool for handling the camouflaged object detection (COD) task. However, most existing methods are heavily dependent on the local spatial information of diverse scales from convolutional operations to optimize initial features. A commonly neglected point in these methods is the long-range dependencies between feature pixels from different scale spaces that can help the model build a global structure of the object, inducing a more precise image representation. In this article, we propose a novel global-local collaborative optimization network called GLCONet. Technically, we first design a collaborative optimization strategy (COS) from the perspective of multisource perception to simultaneously model the local details and global long-range relationships, which can provide features with abundant discriminative information to boost the accuracy in detecting camouflaged objects. Furthermore, we introduce an adjacent reverse decoder (ARD) that contains cross-layer aggregation and reverse optimization to integrate complementary information from different levels for generating high-quality representations. Extensive experiments demonstrate that the proposed GLCONet method with different backbones can effectively activate potentially significant pixels in an image, outperforming 20 state-of-the-art (SOTA) methods on three public COD datasets. The source code is available at: https://github.com/CSYSI/GLCONet.