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http://dx.doi.org/10.7734/COSEIK.2019.32.4.265

Bolt-Loosening Detection using Vision-Based Deep Learning Algorithm and Image Processing Method  

Lee, So-Young (Department of Ocean Engineering, Pukyong National Univ.)
Huynh, Thanh-Canh (Center for Construction, Mechanics and Materials, Institute of Research and Development, Duy Tan Univ.)
Park, Jae-Hyung (Coastal and Structural Solution Co. Ltd.)
Kim, Jeong-Tae (Department of Ocean Engineering, Pukyong National Univ.)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.32, no.4, 2019 , pp. 265-272 More about this Journal
Abstract
In this paper, a vision-based deep learning algorithm and image processing method are proposed to detect bolt-loosening in steel connections. To achieve this objective, the following approaches are implemented. First, a bolt-loosening detection method that includes regional convolutional neural network(RCNN)-based deep learning algorithm and Hough line transform(HLT)-based image processing algorithm are designed. The RCNN-based deep learning algorithm is developed to identify and crop bolts in a connection image. The HLT-based image processing algorithm is designed to estimate the bolt angles from the cropped bolt images. Then, the proposed vision-based method is evaluated for verifying bolt-loosening detection in a lab-scale girder connection. The accuracy of the RCNN-based bolt detector and HLT-based bolt angle estimator are examined with respect to various perspective distortions.
Keywords
bolt-loosening detection; vision; deep learning; RCNN; image processing; hough line transform;
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