Collaborative Camouflaged Object Detection: A Large-Scale Dataset and Benchmark
Cong Zhang, Hongbo Bi, Tian-Zhu Xiang, Ranwan Wu, Jinghui Tong, Xiufang Wang
Abstract
In this article, we provide a comprehensive study of a new task called collaborative camouflaged object detection (CoCOD), which aims to simultaneously detect camouflaged objects with the same properties from a group of relevant images. To this end, we meticulously construct the first large-scale dataset, termed CoCOD8K, which consists of 8528 high-quality and elaborately selected images with object mask annotations, covering five superclasses and 70 subclasses. The dataset spans a wide range of natural and artificial camouflage scenes with diverse object appearances and backgrounds, making it a very challenging dataset for CoCOD. Besides, we propose the first baseline model for CoCOD, named bilateral-branch network (BBNet), which explores and aggregates co-camouflaged cues within a single image and between images within a group, respectively, for accurate camouflaged object detection (COD) in given images. This is implemented by an interimage collaborative feature exploration (CFE) module, an intraimage object feature search (OFS) module, and a local-global refinement (LGR) module. We benchmark 18 state-of-the-art (SOTA) models, including 12 COD algorithms and six CoSOD algorithms, on the proposed CoCOD8K dataset under five widely used evaluation metrics. Extensive experiments demonstrate the effectiveness of the proposed method and the significantly superior performance compared to other competitors. We hope that our proposed dataset and model will boost growth in the COD community. The dataset, model, and results will be available at: https://github.com/zc199823/BBNet-CoCOD.