{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/camouflaged-object-detection","title":"Camouflaged Object Detection","arxiv_id":null,"date":"2020-06-01","proceeding":"CVPR 2020 6","authors":["Deng-Ping Fan"," Ge-Peng Ji"," Guolei Sun"," Ming-Ming Cheng"," Jianbing Shen"," Ling Shao"],"abstract":"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/.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2020/papers/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"camouflaged-object-detection","repo_url":"https://github.com/DengPingFan/CODToolbox","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"camouflaged-object-detection","repo_url":"https://github.com/DengPingFan/SINet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"camouflaged-object-segmentation","task_name":"Camouflaged Object Segmentation"},{"task_slug":"image-matting","task_name":"Image Matting"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[{"slug":"cod10k","name":"COD10K","full_name":"Camouflaged/Concealed Object Detection"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/camouflaged-object-segmentation-on-camo","task":"Camouflaged Object Segmentation","dataset":"CAMO","model":"SINet","rank_in_archive_order":11,"of":14,"metrics":{"MAE":"0.100","S-Measure":"0.751","Weighted F-Measure":"0.606"},"uses_additional_data":false},{"leaderboard":"/sota/camouflaged-object-segmentation-on-cod","task":"Camouflaged Object Segmentation","dataset":"COD","model":"SINet","rank_in_archive_order":11,"of":12,"metrics":{"MAE":"0.092","S-Measure":"0.685","Weighted F-Measure":"0.352"},"uses_additional_data":false},{"leaderboard":"/sota/camouflaged-object-segmentation-on-pcod-1200","task":"Camouflaged Object Segmentation","dataset":"PCOD_1200","model":"SINet-V1","rank_in_archive_order":11,"of":16,"metrics":{"S-Measure":"0.862"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}