Deep neural networks for single shot structured light profilometry

Single-shot 3D imaging and shape reconstruction has seen a surge of interest due to the ever-increasing evolution in sensing technologies. In this paper, a robust single-shot 3D shape reconstruction technique integrating the structured light technique with the deep convolutional neural networks (CNNs) is proposed. The input of the technique is a single fringe-pattern image, and the output is the corresponding depth map for 3D shape reconstruction. The essential training and validation datasets with high-quality 3D ground-truth labels are prepared by using a multi-frequency fringe projection profilometry technique. Unlike the conventional 3D shape reconstruction methods which involve complex algorithms and intensive computation to determine phase distributions or pixel disparities as well as depth map, the proposed approach uses an end-to-end network architecture to directly carry out the transformation of a 2D image to its corresponding 3D depth map without extra processing. In the approach, three CNN-based models are adopted for comparison. Furthermore, an accurate structured-light-based 3D imaging dataset used in this paper is made publicly available. Experiments have been conducted to demonstrate the validity and robustness of the proposed technique. It is capable of satisfying various 3D shape reconstruction demands in scientific research and engineering applications.

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Comparative Analysis of Deep Neural Networks for the Detection and Decoding of Data Matrix Landmarks in Cluttered Indoor Environments

Journal of Intelligent & Robotic Systems ◽

10.1007/s10846-021-01442-x ◽

2021 ◽

Vol 103 (1) ◽

Author(s):

Tiago Almeida ◽

Vitor Santos ◽

Oscar Martinez Mozos ◽

Bernardo Lourenço

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Input Image ◽

Automated Guided Vehicles ◽

Data Matrix ◽

Single Shot ◽

Indoor Environments ◽

Data Set ◽

Valid Solution

AbstractData Matrix patterns imprinted as passive visual landmarks have shown to be a valid solution for the self-localization of Automated Guided Vehicles (AGVs) in shop floors. However, existing Data Matrix decoding applications take a long time to detect and segment the markers in the input image. Therefore, this paper proposes a pipeline where the detector is based on a real-time Deep Learning network and the decoder is a conventional method, i.e. the implementation in libdmtx. To do so, several types of Deep Neural Networks (DNNs) for object detection were studied, trained, compared, and assessed. The architectures range from region proposals (Faster R-CNN) to single-shot methods (SSD and YOLO). This study focused on performance and processing time to select the best Deep Learning (DL) model to carry out the detection of the visual markers. Additionally, a specific data set was created to evaluate those networks. This test set includes demanding situations, such as high illumination gradients in the same scene and Data Matrix markers positioned in skewed planes. The proposed approach outperformed the best known and most used Data Matrix decoder available in libraries like libdmtx.

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Deep Neural Networks for Determining the Parameters of Buildings from Single-Shot Satellite Imagery

Journal of Computer and Systems Sciences International ◽

10.1134/s106423072005007x ◽

2020 ◽

Vol 59 (5) ◽

pp. 755-767

Author(s):

A. N. Trekin ◽

V. Yu. Ignatiev ◽

P. Ya. Yakubovskii

Keyword(s):

Neural Networks ◽

Satellite Imagery ◽

Deep Neural Networks ◽

Single Shot

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SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities

Sensors ◽

10.3390/s20164587 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4587 ◽

Cited By ~ 3

Author(s):

Ángel Morera ◽

Ángel Sánchez ◽

A. Belén Moreno ◽

Ángel D. Sappa ◽

José F. Vélez

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Semantic Segmentation ◽

The Other ◽

Single Shot ◽

Partial Occlusion ◽

True Positive ◽

Detection Problem ◽

Different Types ◽

Outdoor Advertisement

This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included.

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