W
Wooram Choi
Researcher at University of Manitoba
Publications - 10
Citations - 3291
Wooram Choi is an academic researcher from University of Manitoba. The author has contributed to research in topics: Convolutional neural network & Deep learning. The author has an hindex of 8, co-authored 9 publications receiving 1889 citations.
Papers
More filters
Journal ArticleDOI
Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks
TL;DR: This article proposes a vision‐based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features, and shows quite better performances and can indeed find concrete cracks in realistic situations.
Journal ArticleDOI
Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types
TL;DR: A framework for quasi real-time damage detection on video using the trained networks is developed and the robustness of the trained Faster R-CNN is evaluated and demonstrated using 11 new 6,000 × 4,000-pixel images taken of different structures.
Journal ArticleDOI
Vision-based detection of loosened bolts using the Hough transform and support vector machines
TL;DR: In this article, the horizontal and vertical lengths of the bolt head were calculated automatically using the Hough transform and other image processing techniques, and a linear support vector machine was trained with the aforementioned features, thereby building a robust classifier capable of automatically differentiating tight bolts from loose bolts.
Journal ArticleDOI
SDDNet: Real-Time Crack Segmentation
Wooram Choi,Young-Jin Cha +1 more
TL;DR: A pure deep learning method for segmenting concrete cracks in images and shows that the SDDNet segments cracks effectively unless the features are too faint, which is 46 times faster than in a recent work.
Journal ArticleDOI
Fully automated vision-based loosened bolt detection using the Viola–Jones algorithm:
TL;DR: This article proposes a fully automated vision-based method for detecting loosened civil structural bolts using the Viola–Jones algorithm and support vector machines.