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:
this https URL

我们提出了一种边界引导网络（BGNet）来进行伪装目标检测，该方法在三个数据集上都比现有技术高出很多，并利用边缘语义来引导特征学习，从而促进精确边界定位的伪装目标检测。