A GPU-Based Framework for Generating Implicit Datasets of Voxelized Polygonal Models for the Training of 3D Convolutional Neural Networks

Ogáyar, Carlos J.; Rueda, Antonio J.; Segura, Rafael J.; Díaz-Medina, Miguel; Garcia-Fernandez, Angel L.

doi:10.1109/access.2020.2965624

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…The training of the CNN often needs a lot of image data [30][31][32]. Even with an indoor accelerated corrosion test, a large amount of data for training cannot be obtained in a short time.…”

Section: Data Augmentationmentioning

confidence: 99%

Corrosion grade recognition for weathering steel plate based on a convolutional neural network

Wang¹,

Shen²,

Wu³

et al. 2022

Meas. Sci. Technol.

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For the maintenance of weathering steel structure facilities, it is necessary to evaluate the corrosion grade of the rust layer on the surface regularly. At present, the corrosion grade classification of weathering steel is mainly based on the human-eye inspection. In this paper, a deep learning method using a convolutional neural network for evaluating the corrosion grade of weathering steel is proposed to save time and manpower. Firstly, the image dataset of the corrosion steel plate was established using salt spray tests. Then, a CNN architecture named VGG-Corrosion was designed to evaluate the corrosion grade of the corroded steel plate. The effect of the learning rate, transfer learning, and batch size was also investigated to clarify the best hyperparameter configurations to train a powerful corrosion grade classification model. Under the best combination of considered hyperparameters, the mean average accuracy for the corrosion grade evaluation of the test results is 90.96%. The testing results indicated that the CNN based corrosion grade recognition for weath-ering steel plate is prospective, which would be helpful for safety evaluation of steel structures.

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Section: Data Augmentationmentioning

confidence: 99%

Corrosion grade recognition for weathering steel plate based on a convolutional neural network

Wang¹,

Shen²,

Wu³

et al. 2022

Meas. Sci. Technol.

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“…Users can observe the spatial relationship of objects from a full perspective, which can give users a real-world experience and improve the level of scientific management of urban underground pipelines. Compared with the two-dimensional flat map representation, the three-dimensional visualization expression has the characteristics and advantages of strong expressiveness, realistic effects, and clear spatial relationships [4]. e change from two-dimensional to three-dimensional means a change in the way of expressing spatial objects and a deepening of spatial cognition.…”

Section: Introductionmentioning

confidence: 99%

Pipeline 3D Modeling Based on High-Definition Rendering Intelligent Calculation

Li¹,

Zhou²

2022

Mathematical Problems in Engineering

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In the processing of panoramic video, projection mapping is a very critical step. The selection of the projection mapping format will affect the performance, transmission mode, and rendering mode of the panoramic video codec. Therefore, this article starts from the projection mapping format, analyzes the mapping process of the standard mapping format, and then proposes a method of rendering panoramic video in the projection mapping format. By analyzing the parallel design schemes of swarm intelligence algorithms under different granularities, this paper proposes a parallel swarm intelligence optimization algorithm design method and then designs and implements a parallel artificial bee colony algorithm. With the help of the ArcGIS Engine development platform, this paper defines the interface for data exchange. With the support of Multipatch format data in ArcGIS software, through secondary development, the three-dimensional pipeline automatic modeling module is established, and the pipeline model is automatically generated. The digital construction and visualization of the company play a driving role. Based on the understanding of the characteristics of the pipeline image itself, combined with the analysis of the shortcomings of the existing methods, this paper proposes a new deep learning-based high-definition rendering solution for the pipeline image. In this paper, the pipeline image is preprocessed, and then the processed pipeline image is converted into a style pipeline image through the pipeline image style transfer technology, and the obtained style pipeline image is postprocessed to enhance the effect. The preprocessing of pipeline images mainly includes pipeline image enhancement and pipeline image filtering operations. Its purpose is to change the distribution of pipeline images to improve the quality of pipeline images and make them more suitable for subsequent style conversion. In the part of pipeline image style conversion, this paper proposes a new deep learning-based pipeline image high-definition rendering network, which consists of three subnetworks: pipeline image feature modeling module, feature model alignment module, and pipeline image re-rendering module. This article has conducted sufficient experiments to fully compare the processing results of the method proposed in this article and other existing methods and at the same time shows the high-quality high-definition rendering results. The experimental results verify the excellent performance of the method proposed in this paper.

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Lightweight airborne TPMS-filled reflective mirror design for low thermal deformation

Zeng,

Wang,

Hai

et al. 2024

Composite Structures

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A GPU-Based Framework for Generating Implicit Datasets of Voxelized Polygonal Models for the Training of 3D Convolutional Neural Networks

Cited by 6 publications

References 34 publications

Corrosion grade recognition for weathering steel plate based on a convolutional neural network

Corrosion grade recognition for weathering steel plate based on a convolutional neural network

Pipeline 3D Modeling Based on High-Definition Rendering Intelligent Calculation

Lightweight airborne TPMS-filled reflective mirror design for low thermal deformation

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