Occluded Object Segmentation Based on Bilayer Decoupling Strategy and Attention Mechanism

LÜ Yue; ZHOU Zhequan; LÜ Shujing

doi:10.11999/JEIT211288

Volume 45 Issue 1

Jan. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(1): 335-343

LÜ Yue, ZHOU Zhequan, LÜ Shujing. Occluded Object Segmentation Based on Bilayer Decoupling Strategy and Attention Mechanism[J]. Journal of Electronics & Information Technology, 2023, 45(1): 335-343. doi: 10.11999/JEIT211288

Citation:

LÜ Yue, ZHOU Zhequan, LÜ Shujing. Occluded Object Segmentation Based on Bilayer Decoupling Strategy and Attention Mechanism[J]. Journal of Electronics & Information Technology, 2023, 45(1): 335-343. doi: 10.11999/JEIT211288

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Occluded Object Segmentation Based on Bilayer Decoupling Strategy and Attention Mechanism

doi: 10.11999/JEIT211288

LÜ Yue^{1, 3
,
,},
ZHOU Zhequan²,
LÜ Shujing^{1, 3}

1.
School of Communication and Electronic Engineering, East China Normal University, Shanghai 200062, China
2.
School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
3.
Shanghai Key Laboratory of Multidimensional Information Processing, Shanghai 200062, China

Received Date: 2021-11-18
Accepted Date: 2022-03-01
Rev Recd Date: 2022-02-24

Available Online: 2022-03-08

Publish Date: 2023-01-17

Abstract

Abstract

Occluded object segmentation is a difficult problem in instance segmentation, but it has great practical value in many industrial applications such as stacked parcel segmentation on logistics automatic sorting. In this paper, an occluded object segmentation method based on bilayer decoupling strategy and attention mechanism is proposed to improve the segmentation performance of occluded parcels. Firstly, the image features are extracted through a backbone network with a Feature Pyramid Network (FPN); Secondly, the bilayer decoupling head is used to predict whether the mass centers of instances are occluded, and different occlusion types of instances are predicted through different branches; Thirdly, attention refinement module is used to obtain predicted masks of non-occluded instances and generate an attention map by combining these masks; Finally, this attention map is used to help the prediction of occluded instances. A dataset is provided for occluded parcel segmentation. Our method is tested on this dataset. The experimental results show that the proposed network achieves 95,66% Average Precision(AP), 97.17% Recall, and 11.78% Miss Rate(MR^–2). It indicates that this method has better segmentation performance than other methods.
- Image segmentation,
- Attention mechanism,
- Occluded object,
- Bilayer decoupling strategy

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References(22)

References

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