U2-Net: Going deeper with nested U-structure for salient object detection. (October 2020)

• Record Type: Journal Article
• Authors: Qin, Xuebin; Zhang, Zichen; Huang, Chenyang; Dehghan, Masood; Zaiane, Osmar R.; Jagersand, Martin
• Is Part Of: Pattern recognition. Volume 106(2020:Oct.)
• Publication Date: 2020-10
• Subjects: Salient object detection -- Convolutional neural network -- Network architecture design -- Nested U-structure -- Multi-scale feature extraction; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107404
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: 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. Abstract: In this paper, we design a simple yet powerful deep network architecture, U 2 -Net, for salient object detection (SOD). The architecture of our U 2 -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, U 2 -Net (176.3 MB, 30 FPS on GTX 1080Ti GPU) and U 2 -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 .