Single-image depth estimation by refined segmentation and consistency reconstruction

Liu, Huajun; Lei, Dian; Zhu, Qing; Sui, Haigang; Zhang, Huanran; Wang, Ziyan

doi:10.1016/j.image.2020.116048

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…Depending on factors of interest, such as the material and hardness of the subject, various types of images, such as CT images or X-ray radiographic images, can be used to analyze the subject. Various studies, such as medicine, agriculture, and manufacturing, are actively being conducted [25][26][27][28][29][30][31]. These images have the advantage of seeing changes in parameters of interest (e.g., materials constituting the object, hardness, and height) from a photograph of the object at a glance [32].…”

Section: Depth Imagementioning

confidence: 99%

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Kang

Kim

et al. 2021

Applied Sciences

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This paper introduces and implements an efficient training method for deep learning–based anomaly area detection in the depth image of a tire. A depth image of 16 bit integer size is used in various fields, such as manufacturing, industry, and medicine. In addition, the advent of the 4th Industrial Revolution and the development of deep learning require deep learning–based problem solving in various fields. Accordingly, various research efforts use deep learning technology to detect errors, such as product defects and diseases, in depth images. However, a depth image expressed in grayscale has limited information, compared with a three-channel image with potential colors, shapes, and brightness. In addition, in the case of tires, despite the same defect, they often have different sizes and shapes, making it difficult to train deep learning. Therefore, in this paper, the four-step process of (1) image input, (2) highlight image generation, (3) image stacking, and (4) image training is applied to a deep learning segmentation model that can detect atypical defect data. Defect detection aims to detect vent spews that occur during tire manufacturing. We compare the training results of applying the process proposed in this paper and the general training result for experiment and evaluation. For evaluation, we use intersection of union (IoU), which compares the pixel area where the actual error is located in the depth image and the pixel area of the error inferred by the deep learning network. The results of the experiment confirmed that the proposed methodology improved the mean IoU by more than 7% and the IoU for the vent spew error by more than 10%, compared to the general method. In addition, the time it takes for the mean IoU to remain stable at 60% is reduced by 80%. The experiments and results prove that the methodology proposed in this paper can train efficiently without losing the information of the original depth data.

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Section: Depth Imagementioning

confidence: 99%

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Kang

Kim

et al. 2021

Applied Sciences

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“…More recently, Mohaghegh et al [15] combined the global and local features of the scene and devised a modified stacked generalization framework to learn depth values of image patches. Liu et al [16] introduced an efficient monocular depth estimation strategy via an improved segmentation scheme and a consistency constraint. However, if the candidate images and the input image have different depth values in the regions with similar appearance, the problem of depth ambiguity will arise.…”

Section: Related Workmentioning

confidence: 99%

“…The first category casts depth recovery as a non-parametric learning process. Non-parametric approaches can transfer depth to a single input image by leveraging a wide-ranging RGBD dataset efficiently [10][11][12][13][14][15][16]. For an input image, similar candidate images are first retrieved from the RGB-D database by utilizing the k-nearest neighbors algorithm.…”

Section: Introductionmentioning

confidence: 99%

Detail-preserving depth estimation from a single image based on modified fully convolutional residual network and gradient network

Liu

2021

SN Appl. Sci.

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Predicting a convincing depth map from a monocular single image is a daunting task in the field of computer vision. In this paper, we propose a novel detail-preserving depth estimation (DPDE) algorithm based on a modified fully convolutional residual network and gradient network. Specifically, we first introduce a new deep network that combines the fully convolutional residual network (FCRN) and a U-shaped architecture to generate the global depth map. Meanwhile, an efficient feature similarity-based loss term is introduced for training this network better. Then, we devise a gradient network to generate the local details of the scene based on gradient information. Finally, an optimization-based fusion scheme is proposed to integrate the depth and depth gradients to generate a reliable depth map with better details. Three benchmark RGBD datasets are evaluated from the perspective of qualitative and quantitative, the experimental results show that the designed depth prediction algorithm is superior to several classic depth prediction approaches and can reconstruct plausible depth maps.

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“…represents the comparison outputs of our approach and other methods[7,8,[39][40][41][42][43][44] on the Make3D Threshold (th) ∶ percentage of dp, i.e. max…”

mentioning

confidence: 99%

A multiscale dilated convolution and mixed-order attention-based deep neural network for monocular depth prediction

2022

SN Appl. Sci.

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Recovering precise depth information from different scenes has become a popular subject in the semantic segmentation and virtual reality fields. This study presents a multiscale dilated convolution and mixed-order attention-based deep neural network for monocular depth recovery. Specifically, we design a multilevel feature enhancement scheme to enhance and fuse high-resolution and low-resolution features on the basis of mixed-order attention. Moreover, a multiscale dilated convolution module that combines four different dilated convolutions is explored for deriving multiscale information and increasing the receptive field. Recent studies have shown that the design of loss terms is crucial to depth prediction. Therefore, an efficient loss function that combines the ℓ1 loss, gradient loss, and classification loss is also designed to promote rich details. Experiments on three public datasets show that the presented approach achieves better performance than state-of-the-art depth prediction methods.

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Single-image depth estimation by refined segmentation and consistency reconstruction

Cited by 4 publications

References 24 publications

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Detail-preserving depth estimation from a single image based on modified fully convolutional residual network and gradient network

A multiscale dilated convolution and mixed-order attention-based deep neural network for monocular depth prediction

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