Collaborative Camouflaged Object Detection: A Large-Scale Dataset and Benchmark

TL;DR

This paper introduces the first large-scale dataset for collaborative camouflaged object detection (CoCOD), proposes a novel baseline model called BBNet, and benchmarks existing models, significantly advancing research in detecting camouflaged objects across multiple images.

Contribution

The paper presents CoCOD8K, the first large-scale dataset for CoCOD, and introduces BBNet, a baseline model that explores inter-image and intra-image features for improved camouflaged object detection.

Findings

BBNet outperforms 18 state-of-the-art models on CoCOD8K.

The dataset covers diverse natural and artificial camouflage scenarios.

Extensive experiments validate the effectiveness of the proposed approach.

Abstract

In this paper, we provide a comprehensive study on 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 8,528 high-quality and elaborately selected images with object mask annotations, covering 5 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 in given images. This is implemented by an inter-image collaborative feature exploration (CFE) module, an intra-image object feature search (OFS) module, and a local-global refinement (LGR) module. We benchmark 18 state-of-the-art models, including 12 COD algorithms and 6 CoSOD algorithms, on the proposed CoCOD8K dataset under 5 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.