Alternate guidance network for boundary-aware camouflaged object detection

Camouflaged object, similar to the background, shows indistinct boundaries and low-contrast features, which brings great challenges to the detection task. Moreover, existing models still suffer from coarse object boundaries. Motivated by the complementary relationship between boundaries and camouflaged object regions, we propose an alternate guidance network named AGNet for better interaction between them. Specifically, we first propose feature selective module to select high discriminative features and simultaneously filter out noisy background features. The proposed AGNet follows a locate and refine manner, where multi-scale convolution is applied to expand receptive field for accurate initial coarse localization. Finally, a novel alternate guidance module is designed and embedded into each side-output to refine the previous localization progressively. Contributed by it, the complementary characteristic between the region and boundary features can be well captured, which is beneficial to obtain more complete detection. Experimental results on five COD datasets prove the effectiveness of our model, and it is superior to existing state-of-the-art models in object accuracy and boundary accuracy.