Dynamic adaptive scaling network for camouflaged object detection

Recently, accurately segmenting objects from backgrounds has become a significant challenge for Camouflaged Object Detection. Currently, traditional single-view approaches have limitations in predicting the boundary of camouflaged objects. Moreover, a fixed scaling ratio fails to fully explore imperceptible clues between camouflaged objects and background surroundings. To overcome these obstacles, we propose a triplet network via dynamic adaptive scaling strategy, named Dynamic Adaptive Scaling Network for Camouflaged Object Detection(DASNet). It mimics the behavior of humans dynamically adjusting observation distances when observing indistinct objects to zoom in and out images. Specifically, we design a dynamic adaptive scaling module to obtain the optimal scaling ratios for each view. In addition, we design the multi-view feature fusion module and the multi-scale feature fusion module to guide the learning of multi-view and multi-scale features and enhance feature representation. Extensive experiments demonstrate that the proposed DASNet outperforms 24 other representative methods on three public datasets.