基于DeepLab的物体部件分割网络

doi:10.16451/j.cnki.issn1003-6059.202003003

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Abstract The low precision exists in the existing part segmentation, and the generalization and precision can not be balanced. Aiming at the problems, a part segmentation network(DeepLab-MAFE-DSC) based on DeepLab is proposed. A multi-scale adaptive-pattern feature extraction(MAFE) module is proposed in encoder part of the network. The deformable convolution is exploited to enhance the processing capability to irregular contour, and sampling mode of cascade and concatenate in parallel is adopted to balance global and local information simultaneously. A decoder module based on skip connection(DSC) is designed to connect high-level semantic information and low-level character information. Experiments on the dataset show the advantages of DeepLab-MAFE-DSC in simplicity, high part segmentation accuracy and strong generalization.

Key words： Convolutional Neural Network Object Part Segmentation Deformable Convolution

Received: 08 November 2019

ZTFLH:

TP 183

Fund:Supported by Shanghai Aerospace Science and Technology Innovation Foundation(No.SAST2016018)

Corresponding Authors: ZHAO Xia, Ph.D., associate professor. Her research interests include machine vision and sampling control.

About author:: NI Yingting, master student. Her research interests include machine vision.

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ZHAO Xia,NI Yingting. Object Part Segmentation Network Based on DeepLab[J]. , 2020, 33(3): 211-220.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202003003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I3/211

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