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Mixed-Scale Unet Based on Dense Atrous Pyramid for Monocular Depth Estimation
Y. Yang; Y. Wang; C. Zhu; M. Zhu; H. Sun and T. Yan
2021
发表期刊IEEE Access
ISSN21693536
卷号9页码:114070-114084
摘要Monocular depth estimation is an undirected problem, so constructing a network to predict better image depth information is an important research topic. This paper proposes a mixed-scale Unet network (MAPUnet) with a dense atrous pyramid based on the coder-decoder structure widely used in computer vision. We innovatively introduce the Unet++ structure of the image segmentation network for depth estimation. We reset the number of convolutional layers of the network under the framework of the Unet++ network and innovatively connect the decoders densely. Moreover, by choosing the appropriate size of the atrous radius, we form a dense atrous pyramid based on different feature layers to better connect the features in the deep and shallow layers of the network. To verify the effectiveness of the proposed algorithm, we test the network on the KITTI dataset and the NYU Depth V2 dataset. We compare the network with the current state-of-the-art methods. The proposed method has higher accuracy and has steadily improved relative to the threshold of accuracy and root-mean-square error. We also conduct ablation studies, studies targeting the effectiveness of the network framework, and discussions on the convergence time and parameter complexity of the network. We will open-source the code at https://github.com/yang-yi-fan/MAPUnet. 2013 IEEE.
DOI10.1109/ACCESS.2021.3104605
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收录类别SCI ; EI
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文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/65424
专题中国科学院长春光学精密机械与物理研究所
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GB/T 7714
Y. Yang,Y. Wang,C. Zhu,et al. Mixed-Scale Unet Based on Dense Atrous Pyramid for Monocular Depth Estimation[J]. IEEE Access,2021,9:114070-114084.
APA Y. Yang,Y. Wang,C. Zhu,M. Zhu,&H. Sun and T. Yan.(2021).Mixed-Scale Unet Based on Dense Atrous Pyramid for Monocular Depth Estimation.IEEE Access,9,114070-114084.
MLA Y. Yang,et al."Mixed-Scale Unet Based on Dense Atrous Pyramid for Monocular Depth Estimation".IEEE Access 9(2021):114070-114084.
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