2021
DOI: 10.3390/s21206780
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DCPNet: A Densely Connected Pyramid Network for Monocular Depth Estimation

Abstract: Pyramid architecture is a useful strategy to fuse multi-scale features in deep monocular depth estimation approaches. However, most pyramid networks fuse features only within the adjacent stages in a pyramid structure. To take full advantage of the pyramid structure, inspired by the success of DenseNet, this paper presents DCPNet, a densely connected pyramid network that fuses multi-scale features from multiple stages of the pyramid structure. DCPNet not only performs feature fusion between the adjacent stages… Show more

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Cited by 4 publications
(3 citation statements)
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“…CNN-based UNet architecture has become more frequently employed in medical image processing, including classification and segmentation [16,17], in the encoder-decoder CNN design known as UNet, which is based on ignoring the encoder and decoder layers interconnection. These skip connections aim to learn abstract representations of the input data without losing the high-quality features of the input image [18]. Several studies have employed UNet and UNet-based algorithms for BT segmentation, with the BraTS dataset as their image source.…”
Section: Literature Reviewmentioning
confidence: 99%
“…CNN-based UNet architecture has become more frequently employed in medical image processing, including classification and segmentation [16,17], in the encoder-decoder CNN design known as UNet, which is based on ignoring the encoder and decoder layers interconnection. These skip connections aim to learn abstract representations of the input data without losing the high-quality features of the input image [18]. Several studies have employed UNet and UNet-based algorithms for BT segmentation, with the BraTS dataset as their image source.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Neck: Adopts the CSP2 structure from CSPNet to bolster feature fusion capabilities. This structure, rooted in Feature Pyramid Network (FPN) [33,34] and Path Aggregation Network (PAN), performs multi-scale fusion by effectively integrating feature information from different scales [35].…”
Section: Yolov5 Networkmentioning
confidence: 99%
“…With this method, the decoding process can be broken down into different components in order to maximize the benefits of good coding features. In [31], Lai, et al proposed a densely connected pyramid network for monocular depth estimation. By using dense connection modules, they not only integrate the features of adjacent floors, but also integrate the features of non-adjacent floors.…”
Section: The Network Combining Fpn and U-netmentioning
confidence: 99%