Boundary enhancement and refinement network for camouflaged object detection

Abstract

Camouflaged object detection aims to locate and segment objects accurately that conceal themselves well in the environment. Despite the advancements in deep learning methods, prevalent issues persist, including coarse boundary identification in complex scenes and the ineffective integration of multi-source features. To this end, we propose a novel boundary enhancement and refinement network named BERNet, which mainly consists of three modules for enhancing and refining boundary information: an asymmetric edge module (AEM) with multi-groups dilated convolution block (GDCB), a residual mixed pooling enhanced module (RPEM), and a multivariate information interaction refiner module (M2IRM). AEM with GDCB is designed to obtain rich boundary clues, where different dilation rates are used to expand the receptive field. RPEM is capable of enhancing boundary features under the guidance of boundary cues to improve the detection accuracy of small and multiple camouflaged objects. M2IRM is introduced to refine the side-out prediction maps progressively under the supervision of the ground truth by the fusion of multi-source information. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of our BERNet with competitive state-of-the-art methods under the most evaluation metrics.