Wonyong Lee’s research while affiliated with Jeonbuk National University and other places

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Publications (3)


Training pipeline of the proposed method
Distribution of viewpoints. a randomly generated from spherical coordinates, b generated from a Fibonacci sphere
Rendered images and viewpoint candidates. a Top fixed trajectory viewpoints and CAD model images, b Fibonacci viewpoints and point cloud rendering images
Point cloud rendering images according to the different rendering parameters
Multimodal contrastive learning using point clouds and their rendered images
  • Article
  • Publisher preview available

February 2024

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17 Reads

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1 Citation

Wonyong Lee

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In this paper, we propose a novel unsupervised pre-training method for point cloud deep learning models using multimodal contrastive learning. Point clouds, which consist of a set of three-dimensional coordinate points acquired from 3D scanners, lidars, depth cameras, etc. play an important role in representing 3D scenes, and understanding them is crucial for implementing autonomous driving or navigation. Deep learning models based on supervised learning for point cloud understanding require a label for each point cloud data that corresponds to the correct answer in training. However, generating these labels is expensive, making it difficult to build large datasets, which is essential for good model performance. Our proposed unsupervised pre-training method, on the other hand, does not require labels and can serve as an initial value for a model that can alleviate the need for such large datasets. The proposed method is characterized as a multimodal approach that utilizes two modalities for point clouds: the point cloud itself and an image rendering of the point cloud. By using images that directly render the point clouds, the shape information of the point clouds from various viewpoints can be obtained from the images without additional data such as meshes. We pre-trained the model with the proposed method and conducted performance comparison on ModelNet40 and ScanObjectNN datasets. The linear classification accuracy of the point cloud feature vector extracted by the pre-trained model was 91.5% and 83.9%, and after fine-tuning for each dataset, the classification accuracy was 93.3% and 86.9%, respectively.

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Deep-learning-based recognition of symbols and texts at an industrially applicable level from images of high-density piping and instrumentation diagrams

June 2021

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321 Reads

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35 Citations

Expert Systems with Applications

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Wonyong Lee

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[...]

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Duhwan Mun

Piping and instrumentation diagrams (P&IDs) are commonly used in the process industry as a transfer medium for the fundamental design of a plant and for detailed design, purchasing, procurement, construction, and commissioning decisions. The present study proposes a method for symbol and text recognition for P&ID images using deep-learning technology. Our proposed method consists of P&ID image pre-processing, symbol and text recognition, and the storage of the recognition results. We consider the recognition of symbols of different sizes and shape complexities in high-density P&ID images in a manner that is applicable to the process industry. We also standardize the training dataset structure and symbol taxonomy to optimize the developed deep neural network. A training dataset is created based on diagrams provided by a local Korean company. After training the model with this dataset, a recognition test produced relatively good results, with a precision and recall of 0.9718 and 0.9827 for symbols and 0.9386 and 0.9175 for text, respectively.

Citations (1)


... When comparing techniques used to analyze EDs, several authors use convolutional neural networks (CNNs), which are promising given their capabilities to deal with non-linear information and big data. As presented by Kang et al. (2019), other approaches can assist in this analysis, such as the sliding window method and aspect ratio calculation, or according to Kim et al. (2021) using generalized focal loss (GFL). In , they showed how promising it is to divide object detection tasks into stages depending on their class. ...

Reference:

Automatic Digitalization of Railway Interlocking Systems Engineering Drawings Based on Hybrid Machine Learning Methods
Deep-learning-based recognition of symbols and texts at an industrially applicable level from images of high-density piping and instrumentation diagrams
  • Citing Article
  • June 2021

Expert Systems with Applications