Boundary-guided context-aware network for camouflaged object detection

Camouflaged object detection (COD) aims to identify and segment items that are seamlessly assimilate into the surroundings. Compared with the traditional image segmentation, the indefinable boundaries of camouflaged objects and high intrinsic similarities between the targets and the surrounding background make COD more challenging. Although many models have been designed to settle this issue, these algorithms suffer from inaccurate contours. Besides, existing COD methods mainly strive to expand the receptive field and excavate rich contextual cues using convolutional layers with large dilation rates. However, the tiny detailed information may be degenerated in this process due to the large “holes” in these dilated convolutional layers. This paper develops a novel boundary-guided context-aware network (BCNet) to deal with these problems. Specifically, we design a high-resolution feature enhancement module to excavate multi-scale information and enlarge the receptive field without destroying the tiny details within the input feature maps. The proposed module is effective in enhancing the single-layer feature and boosting the COD performance. Besides, a boundary-guided feature interaction module is designed to aggregate multi-level features and investigate the complementary relationship between the targets and the corresponding contours. We evaluate BCNet on four benchmark datasets to verify the effectiveness of the key modules. The experimental results demonstrate that BCNet achieves a real-time inference speed (49 FPS) on a Titan XP GPU and surpasses 19 cutting-edge contenders. Source code will be available at https://github.com/clelouch/BCNet.