A new benchmark for camouflaged object detection: RGB-D camouflaged object detection dataset

Abstract

This article aims to provide a novel image paradigm for camouflaged object detection, i.e., RGB-D images. To promote the development of camouflaged object detection tasks based on RGB-D images, we construct an RGB-D camouflaged object detection dataset, dubbed CODD. This dataset is obtained by converting the existing salient object detection RGB-D datasets by image-to-image translation techniques, which is comparable to the current widely used camouflaged object detection dataset in terms of diversity and complexity. In particular, in order to obtain high-quality translated images, we design a selection strategy that takes into account the structural similarity between pre- and post-conversion images, the similarity between the appearance of objects and their surroundings, as well as the ambiguity of object boundaries. In addition, we extensively evaluate the CODD dataset using existing RGB-D-based salient object detection methods to validate the challenge and usability of the dataset. The CODD dataset will be available at: https://github.com/zcc0616/CODD-Dateset.git.

Keywords

• camouflaged object detection
• rgb-d image
• salient object detection
• image-to-image translation
• selection strategy