Camouflaged Object Detection

Fan, Deng-Ping; Ji, Ge-Peng; Sun, Guolei; Cheng, Ming–Ming; Shen, Jianbing; Shao, Ling

doi:10.1109/cvpr42600.2020.00285

Cited by 469 publications

(388 citation statements)

References 57 publications

Supporting

Mentioning

388

Contrasting

Order By: Relevance

“…This phenomenon is often occurs in nature camouflage objects. In the future, we plan to apply our Inf-Net to other related tasks, such as polyp segmentation [84], product defects detection, camouflaged animal detection [85]. Our code and dataset have been released at: https://github.com/DengPingFan/Inf-Net…”

Section: Resultsmentioning

confidence: 99%

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

Fan

Zhou

et al. 2020

Preprint

Self Cite

216

373

View full text Add to dashboard Cite

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-theart performance.

show abstract

Section: Resultsmentioning

confidence: 99%

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

Fan

Zhou

et al. 2020

Preprint

Self Cite

216

373

View full text Add to dashboard Cite

show abstract

“…), the processes are usually carried out in two steps, namely, search and identification. In [22], Fan et al constructed a network (SINet) which consists of two main components, the search module and the identification module. The search module is mainly used to save the feature information of various levels and the identification module is applied to precisely locate and distinguish camouflaged objects.…”

Section: A Camouflaged Object Detectionmentioning

confidence: 99%

“…Le et al [21] proposed a two-stream (classification stream and detection stream) framework for camouflage object detection. Recently, Fan et al [22] proposed SINet, which simulates the human visual mechanism. Meanwhile, RF [23] structure is used to improve the receptive field of the region to be detected, and satisfactory results are achieved.…”

Section: Introductionmentioning

confidence: 99%

D$^{2}$C-Net: A Dual-Branch, Dual-Guidance and Cross-Refine Network for Camouflaged Object Detection

Wang

Zhang

et al. 2022

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

In this work, we propose a novel framework for camouflaged object detection (COD), named D 2 C-Net, which contains two new modules: dual-branch features extraction (DFE) and gradually refined cross fusion (GRCF). Specifically, the DFE simulates the two-stage detection process of human visual mechanisms in observing camouflage scenes. For the first stage, a dense concatenation is employed to aggregate multi-level features and expand the receptive field. The first stage feature maps are then utilized to extract two-direction guidance information, which benefits the second stage. The GRCF consists of a selfrefine attention unit and a cross-refinement unit, with the aim of combining the peer layer features and DFE features for an improved COD performance. The proposed framework outperforms 13 state-of-the-art deep learning-based methods upon three public datasets in terms of five widely used metrics. Finally, we show evidence for the successful applications of the proposed method in the fields of surface defect detection, medical image segmentation.

show abstract

“…A comprehensive list of work for COVID‐19 image‐based AI techniques are found in Maga et al 18 and Fu et al 25 Developing a deep network gives more benefits for automatic and fast segmentation of the medical images 26 . Camouflaged object detection (COD) is introduced to identify the embedded object with their surroundings 27 . COD is beneficial in medical image applications such as lung infection segmentation.…”

Section: Related Work On the Covid‐19 Using Aimentioning

confidence: 99%

An integrated feature frame work for automated segmentation of COVID‐19 infection from lung CT images

Selvaraj

Venkatesan

Mahesh

et al. 2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

The novel coronavirus disease (SARS‐CoV‐2 or COVID‐19) is spreading across the world and is affecting public health and the world economy. Artificial Intelligence (AI) can play a key role in enhancing COVID‐19 detection. However, lung infection by COVID‐19 is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets. Segmentation is a preferred technique to quantify and contour the COVID‐19 region on the lungs using computed tomography (CT) scan images. To address the dataset problem, we propose a deep neural network (DNN) model trained on a limited dataset where features are selected using a region‐specific approach. Specifically, we apply the Zernike moment (ZM) and gray level co‐occurrence matrix (GLCM) to extract the unique shape and texture features. The feature vectors computed from these techniques enable segmentation that illustrates the severity of the COVID‐19 infection. The proposed algorithm was compared with other existing state‐of‐the‐art deep neural networks using the Radiopedia and COVID‐19 CT Segmentation datasets presented specificity, sensitivity, sensitivity, mean absolute error (MAE), enhance‐alignment measure (EM φ ), and structure measure ( S m ) of 0.942, 0.701, 0.082, 0.867, and 0.783, respectively. The metrics demonstrate the performance of the model in quantifying the COVID‐19 infection with limited datasets.

show abstract

Camouflaged Object Detection

Cited by 469 publications

References 57 publications

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

D$^{2}$C-Net: A Dual-Branch, Dual-Guidance and Cross-Refine Network for Camouflaged Object Detection

An integrated feature frame work for automated segmentation of COVID‐19 infection from lung CT images

Contact Info

Product

Resources

About