Finding Camouflaged Object Guided by Contour and Attention

Camouflaged object detection aims to detect objects closely blended into the background. Inspired by visual mechanism of human being, we define detection process as an end-to-end task consisted of locating and refining. To this end, we propose Contour Supervision and Initial Locating Guidance Network (CSIGNet) to effectively segment camouflaged objects from background. Specifically, our method fully explores the contribution of semantic contour to the binary segmentation task. In addition, attention mechanism is used for our final prediction. Experiments show that it has achieved excellent results on public datasets and the model can accurately segment camouflage objects. Codes will be made available at https://github.com/RecKono/CSIGNet.