1 research outputs found
Depth-Guided Camouflaged Object Detection
Camouflaged object detection (COD) aims to segment camouflaged objects hiding
in the environment, which is challenging due to the similar appearance of
camouflaged objects and their surroundings. Research in biology suggests that
depth can provide useful object localization cues for camouflaged object
discovery, as all the animals have 3D perception ability. However, the depth
information has not been exploited for camouflaged object detection. To explore
the contribution of depth for camouflage detection, we present a depth-guided
camouflaged object detection network with pre-computed depth maps from existing
monocular depth estimation methods. Due to the domain gap between the depth
estimation dataset and our camouflaged object detection dataset, the generated
depth may not be accurate enough to be directly used in our framework. We then
introduce a depth quality assessment module to evaluate the quality of depth
based on the model prediction from both RGB COD branch and RGB-D COD branch.
During training, only high-quality depth is used to update the modal
interaction module for multi-modal learning. During testing, our depth quality
assessment module can effectively determine the contribution of depth and
select the RGB branch or RGB-D branch for camouflage prediction. Extensive
experiments on various camouflaged object detection datasets prove the
effectiveness of our solution in exploring the depth information for
camouflaged object detection. Our code and data is publicly available at:
\url{https://github.com/JingZhang617/RGBD-COD}.Comment: 10 pages main content + 3 pages reference. The first work in RGB-D
Camouflaged object detection (COD