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具有增强注意力的前景分割网络 by Rui Jiang; Ruixiang Zhu; Xiaocui Cai; Hu Su is a Engineering article available to read on EtoBox.

What is 具有增强注意力的前景分割网络 about?

Moving object segmentation (MOS) is one of the essential functions of the vision system of all robots, including medical robots. Deep learning-based MOS methods, especially deep end-to-end MOS methods, are actively investigated in this field. Foreground segmentation networks (FgSegNets) are representative deep end-to-end MOS methods proposed recently. This study explores a new mechanism to improve the spatial feature learning capability of FgSegNets with relatively few brought parameters. Specifically, we propose an enhanced attention (EA) module, a parallel connection of an attention module and a lightweight enhancement module, with sequential attention and residual attention as special cases. We also propose integrating EA with FgSegNet v2 by taking the lightweight convolutional block attention module as the attention module and plugging EA module after the two Maxpooling layers of the encoder. The derived new model is named FgSegNet v2 EA. The ablation study verifies the effectiveness of the proposed EA module and integration strategy. The results on the CDnet2014 dataset, which depicts human activities and vehicles captured in different scenes, show that FgSegNet v2 EA outperfo

Who reads 具有增强注意力的前景分割网络?

Author

Rui Jiang; Ruixiang Zhu; Xiaocui Cai; Hu Su

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Engineering (Physical Sciences)