Journal of the Computational Structural Engineering Institute of Korea (한국전산구조공학회논문집)

Computational Structural Engineering Institute of Korea (한국전산구조공학회)
권호 표지
DOI QR코드

DOI QR Code

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

영상기반 딥러닝 및 이미지 프로세싱 기법을 이용한 볼트풀림 손상 검출

  • 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.)
  • Received : 2019.07.08
  • Accepted : 2019.07.29
  • Published : 2019.08.31
Download PDF
⟨ Previous Next ⟩

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.

본 연구에서는 영상기반 딥러닝 및 이미지 프로세싱 기법을 이용한 볼트풀림 손상검출 기법을 제안하였다. 이를 위해 먼저, 딥러닝 및 이미지 프로세싱 기반 볼트풀림 검출 기법을 설계하였다. 영상기반 볼트풀림 검출 기법은 볼트 이미지 검출 과정 및 볼트풀림 각도 추정 과정으로 구성된다. 볼트 이미지의 검출을 위하여 RCNN기반 딥러닝 알고리즘을 이용하였다. 영상의 원근왜곡 교정을 위해 호모그래피 개념을 이용하였으며 볼트풀림 각도를 추정을 위하여 Hough 변환을 이용하였다. 다음으로 제안된 기법의 성능을 검증을 위하여 거더의 볼트 연결부 모형을 대상으로 볼트풀림 손상검출 실험을 수행하였다. 다양한 원근 왜곡 조건에 대하여 RCNN 기반 볼트 검출기와 Hough 변환 기반 볼트풀림 각도 추정기의 성능을 검토하였다.

Keywords

References

  1. KOSTAT (2019) E-nara index, http://www.index.go.kr/.
  2. Abdeljaber, O., Avci, O., Kiranyaz, M.S., Boashash, B., Sodano, H., Inman, D.J. (2018) 1-D CNNs for Structural Damage Detection: Verification on a Structural Health Monitoring Benchmark Data, Neurocomput., 275, pp.1308-1317. https://doi.org/10.1016/j.neucom.2017.09.069
  3. Cha, Y.J., Choi, W., Buyukozturk, O. (2017) Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks, Comput.-Aided Civil & Infrastruct. Eng., 32(5), pp.361-378. https://doi.org/10.1111/mice.12263
  4. Cha, Y.J., You, K., Choi, W. (2016) Vision-based Detection of Loosened Bolts using the Hough Transform and Support Vector Machines, Autom. Constr., 71, pp.181-188. https://doi.org/10.1016/j.autcon.2016.06.008
  5. Girshick, R., Donahue, J., Darrell, T., Malik, J. (2014) Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, 2014 IEEE Conf. Comput. Vision & Pattern Recognit., Jun. 2014, Columbus, USA.
  6. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, L., Wang, G. (2015) Recent Advances in Convolutional Neural Networks, Pattern Recognit., 77, pp.354-377. https://doi.org/10.1016/j.patcog.2017.10.013
  7. Janssens, O., Slavkovikj, V., Vervisch, B., Stockman, K., Loccufier, M., Verstockt, S., Van de Walle, R., Van Hoecke, S. (2016) Convolutional Neural Network Based Fault Detection for Rotating Machinery, J. Sound & Vib., 377, pp.331-345. https://doi.org/10.1016/j.jsv.2016.05.027
  8. Krizhevsky, A., Hinton, G. (2009) Learning Multiple Layers of Features from Tiny Images, Technical Report TR-2009, University of Toronto, Toronto.
  9. Nguyen, T.C., Huynh, T.C., Ryu, J.Y., Park, J.H., Kim, J.T. (2016) Bolt-loosening Identification of Bolt Connections by Vision Image-based Technique, SPIE Smart Struct. & Mater. + Nondestruc. Eval. & Health Monit., Mar. 2016, Las Vegas, USA.
  10. Park, J.H., Huynh, T.C., Choi, S.H., Kim, J.T. (2015) Vision-based Technique for Bolt-loosening Detection in Wind Turbine Tower, Wind & Struct., 21(6), pp.709-726. https://doi.org/10.12989/was.2015.21.6.709
  11. Yang, S., Ho, C.C., Chen, J., Chang, C. (2012) Practical Homography-Based Perspective Correction Method for License plate Recognition, 2012 Int. Conf. Inf. Secur. & Intell. Control, Aug. 2012, Yunlin, Taiwan.
  12. Zhao, X., Zhang, Y., Wang, N. (2019) Bolt Loosening Angle Detection Technology using Deep Learning, Struct. Control & Health Monit., 26(1), https://doi.org/10.1002/stc.2292.