Monocular Depth Prediction With Residual DenseASPP Network

Wu, Kunpeng; Zhang, Shunran; Xie, Zhen

doi:10.1109/access.2020.3006704

Cited by 5 publications

(5 citation statements)

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“…In recent years, deep convolutional networks have been applied to depth estimation and have achieved excellent results such as [2][3][4][5][6][7][8][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Now we generally considered that the beginning of the depth estimation of a single image based on deep learning is Eigen et al [2].…”

Section: A Monocular Depth Estimationmentioning

confidence: 99%

“…Yuru et al [24] added an attention mechanism to the classification algorithm, combined with contextual content, and it also used the soft classification method to improve the quality of prediction depth. Wu et al [23] applied (Atrous Spatial Pyramid Pooling) ASPP to depth estimation tasks. It used ASPP convolution kernels of different sizes to obtain feature information of different scales, which achieved excellent estimation results.…”

Section: A Monocular Depth Estimationmentioning

confidence: 99%

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Monocular Depth Estimation Based on Multi-Scale Depth Map Fusion

et al. 2021

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Monocular depth estimation is a basic task in machine vision. In recent years, the performance of monocular depth estimation has been greatly improved. However, most depth estimation networks are based on a very deep network to extract features that lead to a large amount of information lost. The loss of object information is particularly serious in the encoding and decoding process. This information loss leads to the estimated depth maps lacking object structure detail and have non-clear edges. Especially in a complex indoor environment, which is our research focus in this paper, the consequences of this loss of information are particularly serious. To solve this problem, we propose a Dense feature fusion network that uses a feature pyramid to aggregate various scale features. Furthermore, to improve the fusion effectiveness of decoded object contour information and depth information, we propose an adaptive depth fusion module, which allows the fusion network to fuse various scale depth maps adaptively to increase object information in the predicted depth map. Unlike other work predicting depth maps relying on U-NET architecture, our depth map predicted by fusing multi-scale depth maps. These depth maps have their own characteristics. By fusing them, we can estimate depth maps that not only include accurate depth information but also have rich object contour and structure detail. Experiments indicate that the proposed model can predict depth maps with more object information than other prework, and our model also shows competitive accuracy. Furthermore, compared with other contemporary techniques, our method gets state-of-the-art in edge accuracy on the NYU Depth V2 dataset.

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Section: A Monocular Depth Estimationmentioning

confidence: 99%

Section: A Monocular Depth Estimationmentioning

confidence: 99%

Monocular Depth Estimation Based on Multi-Scale Depth Map Fusion

et al. 2021

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“…The U-Net architecture consists of two parts, namely the encoder, and the decoder. The encoder section functions to extract features from the image while the decoder functions to reconstruct image features [18]. Several studies that have used the U-Net architecture have been carried out.…”

Section: Introductionmentioning

confidence: 99%

“…The inclusion of skip connections might lead to the oversight or loss of several significant features and information from the preceding layers. [18]. The skip connection feature which causes a lot of information in the previous layer to be missed or lost (vanishing gradient) can be overcome by modifying the Xception architecture.…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage CNN: U-Net and Xcep-Dense of Glaucoma Detection in Retinal Images

Desiani,

Priyanta,

Ramayanti

et al. 2023

j.electron.electromedical.eng.med.inform

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Glaucoma is a chronic neurological disease in the human eye where there is damage to the nerves which causes vision loss to blindness. Glaucoma can be detected by classifying retinal images. Several previous studies that classified glaucoma did not perform segmentation beforehand. Segmentation is needed to extract the features of the optic disc and optic cup from retinal images that are used to detect glaucoma. This study proposes two stages in the detection of glaucoma, namely the segmentation and classification stages. Segmentation is carried out using the U-Net architecture. Classification is done using a new architecture, namely Xcep-Dense. The Xcep-Dense architecture is a new architecture which is the result of a combination of the Xception and DenseNet architectures. At the segmentation stage, accuracy, recall, precision, and F1-score values are obtained above 90%. The Cohen’s kappa value has a value above 85% and loss below 20%. At the classification stage, accuracy and specification values were obtained above 85%, sensitivity and F1-score above 80%, and Cohen’s kappa above 70%. The predicted image obtained at the segmentation stage has a very similar appearance to the ground truth. Based on the results of the performance evaluation obtained, it shows that the method proposed in this study is feasible in detecting glaucoma.Glaucoma,

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“…Eigen et al [3] proposed the first monocular depth estimation method base on deep learning, which showed a surprising performance than pre-works [1] [2]. Then, a lot of excellent works based on deep learning were proposed, such as [4] [5] [6] [7] [8] [9] [10] [11]. However, monocular depth estimation methods still suffered from the boundary blur Fig.…”

mentioning

confidence: 99%

Monocular Depth Estimation with Sharp Boundary

Yang¹,

Chang²,

Chen³

2021

Preprint

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Monocular depth estimation is the base task in computer vision. It has a tremendous development in the decade with the development of deep learning. But the boundary blur of the depth map is still a serious problem. Research finds the boundary blur problem is mainly caused by two factors, first, the low-level features containing boundary and structure information may loss in deeper networks during the convolution process., second, the model ignores the errors introduced by the boundary area due to the few portions of the boundary in the whole areas during the backpropagation. In order to mitigate the boundary blur problem, we focus on the above two impact factors. Firstly, we design a scene understanding module to learn the global information with low-and high-level features, and then to transform the global information to different scales with our proposed scale transform module according to the different phases in decoder. Secondly, we propose a boundary-aware depth loss function to pay attention to the effects of the boundary's depth value. The extensive experiments show that our method can predict the depth maps with clearer boundaries, and the performance of the depth accuracy base on NYU-depth v2 and SUN RGB-D is competitive.

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Monocular Depth Prediction With Residual DenseASPP Network

Cited by 5 publications

References 50 publications

Monocular Depth Estimation Based on Multi-Scale Depth Map Fusion

Monocular Depth Estimation Based on Multi-Scale Depth Map Fusion

Multi-Stage CNN: U-Net and Xcep-Dense of Glaucoma Detection in Retinal Images

Monocular Depth Estimation with Sharp Boundary

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