Camouflaged/Concealed Object Detection (2022-08-14)

Application: Link



Polyp Segmentation
Art Design
Image Retrieval
Defect Detection
Search and rescue system
Translation between camouflaged objects and salient objects


Sensory ecologists have found that this s background matching camouflage strategy works by deceiving the visual perceptual system of the observer. Naturally, addressing concealed object detection (COD) requires a significant amount of visual perception knowledge. Understanding COD has not only scientific value in itself, but it also important for applications in many fundamental fields, such as computer vision (e.g., for search-and-rescue work, or rare species discovery), medicine (e.g., polyp segmentation, lung infection segmentation), agriculture (e.g., locust detection to prevent invasion), and art (e.g., recreational art). The high intrinsic similarities between the targets and non-targets make COD far more challenging than traditional object segmentation/detection. Although it has gained increased attention recently, studies on COD still remain scarce, mainly due to the lack of a sufficiently large dataset and a standard benchmark like Pascal-VOC, ImageNet, MS-COCO, ADE20K, and DAVIS.

To build the large-scale COD dataset, we build the COD10K, which contains 10,000 images (5,066 camouflaged, 3,000 background, 1,934 noncamouflaged), divided into 10 super-classes, and 78 sub-classes (69 camouflaged, nine non-camouflaged) which are collected from multiple photography websites.


Groups and Categories


Dataset Statistics


Annotation Quality


Label Diversity


Object Diversity


Data Attributes


Training Set (6,000)

The training set (COD10K-Tr) contains 6,000 images including 3,040 camouflaged images and 2,960 non-camouflaged images.


Test Set (4,000)

The test set (COD10K-Te) contains 4,000 images including 2,026 camoufalged images and 1,974 non-camouflaged images.


Non-Camouflaged Images (4,000)

The Non-Camouflaged set (COD10K-NonCAM) can be used for the contrast learning model.


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Qualitative Comparisons Against SOTAs.


Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, Ling Shao. Concealed Object Detection. TPAMI, 2022.[PDF][中译版][Github][COD10K Dataset]

Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, Ling Shao. Camouflaged Object Detection. CVPR, 2020.[PDF][中译版][Github]


               author={Fan, Deng-Ping and Ji, Ge-Peng and Cheng, Ming-Ming and Shao, Ling},
               title={Concealed object detection},
               journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},

               author={Fan, Deng-Ping and Ji, Ge-Peng and Sun, Guolei and Cheng, Ming-Ming and Shen, Jianbing and Shao, Ling},
               title={Camouflaged object detection},