Namgyu Kim
Sejong University
19 Papers
30 Citations
Namgyu Kim is an academic researcher from Sejong University. The author has contributed to research in topics: Ground-penetrating radar & Computer science. The author has an hindex of 8, co-authored 19 publications.
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Papers
Deep learning–based autonomous concrete crack evaluation through hybrid image scanning:
TL;DR: The proposed deep learning–based autonomous concrete crack detection technique is able to achieve automated crack identification and visualization by transfer learning of a well-trained deep convolutional neural network, that is, GoogLeNet, while retaining the advantages of the hybrid images.
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Automated pavement distress detection using region based convolutional neural networks
TL;DR: This study proposes a method for detecting signs of pavement distress based on faster region based convolutional neural network (Faster R-CNN), which could successfully detect cracks and partial patching with accuracy in pavement images.
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Deep learning-based automated underground cavity detection using three-dimensional ground penetrating radar:
TL;DR: In this article, three-dimensional ground- penetrating radar data are often ambiguous and complex to interpret when attempting to detect only underground cavities because ground penetrating radar reflections from reflections from va...
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A novel 3D GPR image arrangement for deep learning-based underground object classification
TL;DR: A novel deep learning-based underground object classification method is proposed by using two-dimensional grid image which consists of several B-scan and C-scan images, which successfully classifies cavity, pipe, manhole and subsoils background having very small false-positive errors.
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Deep learning-based underground object detection for urban road pavement
TL;DR: In this article, ground penetrating radar (GPR) is used for detecting buried underground objects in urban area, and deep learning technique is applied into deep learning for detecting underground objects.
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