  Camouflaged object detection (COD), segmenting objects that are elegantly
blended into their surroundings, is a valuable yet challenging task. Existing
deep-learning methods often fall into the difficulty of accurately identifying
the camouflaged object with complete and fine object structure. To this end, in
this paper, we propose a novel boundary-guided network (BGNet) for camouflaged
object detection. Our method explores valuable and extra object-related edge
semantics to guide representation learning of COD, which forces the model to
generate features that highlight object structure, thereby promoting
camouflaged object detection of accurate boundary localization. Extensive
experiments on three challenging benchmark datasets demonstrate that our BGNet
significantly outperforms the existing 18 state-of-the-art methods under four
widely-used evaluation metrics. Our code is publicly available at:
https://github.com/thograce/BGNet.
