Papers › Camouflaged Object Detection
Camouflaged Object Detection
Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, Ling Shao
We present a comprehensive study on a new task named camouflaged object detection (COD), which aims to identify objects that are "seamlessly" embedded in their surroundings. The high intrinsic similarities between the target object and the background make COD far more challenging than the traditional object detection task. To address this issue, we elaborately collect a novel dataset, called COD10K, which comprises 10,000 images covering camouflaged objects in various natural scenes, over 78 object categories. All the images are densely annotated with category, bounding-box, object-/instance-level, and matting-level labels. This dataset could serve as a catalyst for progressing many vision tasks, e.g., localization, segmentation, and alpha-matting, etc. In addition, we develop a simple but effective framework for COD, termed Search Identification Network (SINet). Without any bells and whistles, SINet outperforms various state-of-the-art object detection baselines on all datasets tested, making it a robust, general framework that can help facilitate future research in COD. Finally, we conduct a large-scale COD study, evaluating 13 cutting-edge models, providing some interesting findings, and showing several potential applications. Our research offers the community an opportunity to explore more in this new field. The code will be available at https://github.com/DengPingFan/SINet/.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Camouflaged Object Segmentation | CAMO | SINet | MAE | 0.100 | #11 of 14 | Archive leaderboard | report |
| Camouflaged Object Segmentation | CAMO | SINet | S-Measure | 0.751 | #11 of 14 | Archive leaderboard | report |
| Camouflaged Object Segmentation | CAMO | SINet | Weighted F-Measure | 0.606 | #11 of 14 | Archive leaderboard | report |
| Camouflaged Object Segmentation | COD | SINet | MAE | 0.092 | #11 of 12 | Archive leaderboard | report |
| Camouflaged Object Segmentation | COD | SINet | S-Measure | 0.685 | #11 of 12 | Archive leaderboard | report |
| Camouflaged Object Segmentation | COD | SINet | Weighted F-Measure | 0.352 | #11 of 12 | Archive leaderboard | report |
| Camouflaged Object Segmentation | PCOD_1200 | SINet-V1 | S-Measure | 0.862 | #11 of 16 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections