U2-Net: Going deeper with nested U-structure for salient object detection

•A novel ReSidual U-block (RSU) is designed to capture multi-scale deep features.•A nested U-structure, called U2-Net, that uses RSU is developed for salient object detection.•Both large (176.3 MB) and small (4.7 MB) instances of U2-Net get competitive results. In this paper, we design a simple yet...

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Bibliographic Details
Published in:	Pattern recognition Vol. 106; p. 107404
Main Authors:	Qin, Xuebin, Zhang, Zichen, Huang, Chenyang, Dehghan, Masood, Zaiane, Osmar R., Jagersand, Martin
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.10.2020
Subjects:	Convolutional neural network Multi-scale feature extraction Nested U-structure Network architecture design Salient object detection Nested U-structure Network architecture design Salient object detection Convolutional neural network Multi-scale feature extraction
ISSN:	0031-3203, 1873-5142
Online Access:	Get full text
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Abstract	•A novel ReSidual U-block (RSU) is designed to capture multi-scale deep features.•A nested U-structure, called U2-Net, that uses RSU is developed for salient object detection.•Both large (176.3 MB) and small (4.7 MB) instances of U2-Net get competitive results. In this paper, we design a simple yet powerful deep network architecture, U2-Net, for salient object detection (SOD). The architecture of our U2-Net is a two-level nested U-structure. The design has the following advantages: (1) it is able to capture more contextual information from different scales thanks to the mixture of receptive fields of different sizes in our proposed ReSidual U-blocks (RSU), (2) it increases the depth of the whole architecture without significantly increasing the computational cost because of the pooling operations used in these RSU blocks. This architecture enables us to train a deep network from scratch without using backbones from image classification tasks. We instantiate two models of the proposed architecture, U2-Net (176.3 MB, 30 FPS on GTX 1080Ti GPU) and U2-Net† (4.7 MB, 40 FPS), to facilitate the usage in different environments. Both models achieve competitive performance on six SOD datasets. The code is available: https://github.com/NathanUA/U-2-Net.
AbstractList	•A novel ReSidual U-block (RSU) is designed to capture multi-scale deep features.•A nested U-structure, called U2-Net, that uses RSU is developed for salient object detection.•Both large (176.3 MB) and small (4.7 MB) instances of U2-Net get competitive results. In this paper, we design a simple yet powerful deep network architecture, U2-Net, for salient object detection (SOD). The architecture of our U2-Net is a two-level nested U-structure. The design has the following advantages: (1) it is able to capture more contextual information from different scales thanks to the mixture of receptive fields of different sizes in our proposed ReSidual U-blocks (RSU), (2) it increases the depth of the whole architecture without significantly increasing the computational cost because of the pooling operations used in these RSU blocks. This architecture enables us to train a deep network from scratch without using backbones from image classification tasks. We instantiate two models of the proposed architecture, U2-Net (176.3 MB, 30 FPS on GTX 1080Ti GPU) and U2-Net† (4.7 MB, 40 FPS), to facilitate the usage in different environments. Both models achieve competitive performance on six SOD datasets. The code is available: https://github.com/NathanUA/U-2-Net.
ArticleNumber	107404
Author	Qin, Xuebin Dehghan, Masood Zhang, Zichen Jagersand, Martin Huang, Chenyang Zaiane, Osmar R.
Author_xml	– sequence: 1 givenname: Xuebin orcidid: 0000-0002-9042-7192 surname: Qin fullname: Qin, Xuebin email: xuebin@ualberta.ca – sequence: 2 givenname: Zichen surname: Zhang fullname: Zhang, Zichen email: zichen2@ualberta.ca – sequence: 3 givenname: Chenyang orcidid: 0000-0003-2811-6008 surname: Huang fullname: Huang, Chenyang email: chuang8@ualberta.ca – sequence: 4 givenname: Masood surname: Dehghan fullname: Dehghan, Masood email: masood1@ualberta.ca – sequence: 5 givenname: Osmar R. surname: Zaiane fullname: Zaiane, Osmar R. email: zaiane@ualberta.ca – sequence: 6 givenname: Martin surname: Jagersand fullname: Jagersand, Martin email: mj7@ualberta.ca
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Cites_doi	10.1016/j.patcog.2017.11.024 10.1109/TIP.2016.2602079 10.1016/j.patcog.2019.03.023 10.1016/j.patcog.2016.10.025
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Snippet	•A novel ReSidual U-block (RSU) is designed to capture multi-scale deep features.•A nested U-structure, called U2-Net, that uses RSU is developed for salient...
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SubjectTerms	Convolutional neural network Multi-scale feature extraction Nested U-structure Network architecture design Salient object detection
Title	U2-Net: Going deeper with nested U-structure for salient object detection
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