[논문]Detection and quantification of bolt loosening using RGB-D camera and Mask R-CNN

Chung, Junyeon; Sohn, Hoon

doi:10.12989/sss.2021.27.5.783

[국내논문] Detection and quantification of bolt loosening using RGB-D camera and Mask R-CNN

Smart structures and systems, v.27 no.5, 2021년, pp.783 - 793

Chung, Junyeon (Department of Civil and Environmental Engineering, Korea Advanced Institute for Science and Technology) , Sohn, Hoon (Department of Civil and Environmental Engineering, Korea Advanced Institute for Science and Technology)

Abstract ▼ AI-Helper

Bolt loosening is one of the most common types of damage for bolt-connected plates. Existing vision techniques detect bolt loosening based on the measurement of bolt rotation or the exposure of bolt threads. However, these techniques examine bolt tightness only in a qualitative manner, or require a reference measurement at the initially tightened state of the bolt for quantitative estimation. In this study, the exposed shank length of a bolt is quantitatively measured using an RGB-depth camera and a mask-region-based convolutional neural network but without requiring any measurement from the initial state of the bolt. The performance of the proposed technique is validated by conducting lab-scale experiments, in which the angle and distance of the camera are varied with respect to a target inspection area. The proposed technique successfully detects bolt loosening at exposed shank length over 3 mm with a resolution of 1 mm and 97% accuracy at different camera angles (40°-90°) and distances (up to 65 cm).

Keyword

참고문헌 (28)

Benkhoui, Y., El Korchi, T. and Reinhold, L. (2019), "UAS-based crack detection using stereo cameras: a comparative study", Proceedings of International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, GA, USA, June.
Cha, Y.-J., You, K. and Choi, W. (2016), "Vision-based detection of loosened bolts using the Hough transform and support vector machines", Autom. Constr., 71(2), 181-188. https://doi.org/10.1016/j.autcon.2016.06.008

상세보기
Dubois, E. and Sabri, S. (1984), "Noise reduction in image sequences using motion-compensated temporal filtering", IEEE Trans. Commun., 32(7), 826-831. https://doi.org/10.1109/TCOM.1984.1096143

상세보기
Fischler, M.A. and Bolles, R.C. (1981), "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography", Commun. ACM, 24(6), 381-395. https://doi.org/10.1145/358669.358692

상세보기
Grunnet-Jepsen, A., Sweetser, J.N., Winer, P., Takagi, A. and Woodfill, J. (2018), "Projectors for Intel® RealSenseTM Depth Cameras D4xx", Intel.
Hartley, R. and Zisserman, A. (2003), Multiple View Geometry in Computer Vision, Cambridge University Press, Cambridge, United Kingdom.
He, K., Zhang, X., Ren, S. and Sun, J. (2016), "Deep residual learning for image recognition", IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, June.
He, K., Gkioxari, G., Dollar, P. and Girshick, R. (2017), "Mask R-CNN", IEEE International Conference on Computer Vision, Venice, Italy, October.
Huynh, T.-C. and Kim, J.-T. (2017), "Quantification of temperature effect on impedance monitoring via PZT interface for prestressed tendon anchorage", Smart Mater. Struct., 26(12), 125004. https://doi.org/10.1088/1361-665X/aa931b

상세보기
Huynh, T. and Kim, J. (2018), "RBFN-based temperature compensation method for impedance monitoring in prestressed tendon anchorage", Struct. Control Health Monit., 25(6), e2173. https://doi.org/10.1002/stc.2173

상세보기
Huynh, T.-C., Dang, N.-L. and Kim, J.-T. (2018), "Preload monitoring in bolted connection using piezoelectric-based smart interface", Sensors, 18(9), 2766. https://doi.org/10.3390/s18092766

상세보기
Huynh, T.-C., Park, J.-H., Jung, H.-J. and Kim, J.-T. (2019), "Quasi-autonomous bolt-loosening detection method using vision-based deep learning and image processing", Autom. Constr., 105, 102844. https://doi.org/10.1016/j.autcon.2019.102844

상세보기
Korea Expressway Corporation (2013), "Improvement of bridge inspection system by the damage analysis", Korea Expressway Corporation.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J, Perona, P., Ramanan, D., Dollar, P. and Zitnick C.L. (2014), "Microsoft COCO: common objects in context", Proceedings of European Conference on Computer Vision, Zurich, Switzerland, September.
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B. and Belongie, S. (2017), "Feature pyramid networks for object detection", Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, July.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S. and Cheng-Yang, F. (2016), "SSD: single shot multibox detector", Proceedings of European Conference on Computer Vision, Amsterdam, The Netherlands, October.
Park, J.-H., Huynh, T.-C., Choi, S.-H. and Kim, J.-T. (2015), "Vision-based technique for bolt-loosening detection in wind turbine tower", Wind Struct., Int. J., 21(6), 709-726. https://doi.org/10.12989/was.2015.21.6.709

상세보기
Ramana, L., Choi, W. and Cha, Y.-J. (2019), "Fully automated vision-based loosened bolt detection using the Viola-Jones algorithm", Struct. Health Monit., 18(2), 422-434. https://doi.org/10.1177/1475921718757459

상세보기
Redmon, J. and Farhadi, A. (2018), "YOLOv3: An incremental improvement", arXiv Prepr. arXiv1804.02767.
Ren, S., He, K., Girshick, R. and Sun, J. (2015), "Faster R-CNN: towards real-time object detection with region proposal networks", Neural Inform. Process. Syst., Montreal, Canada, December.
Simonyan, K. and Zisserman, A. (2014), "Very deep convolutional networks for large-scale image recognition", arXiv Prepr. arXiv1409.1556.
Suda, M., Hasuo, Y., Kanaya, A., Ogura, Y. Takishita, T. and Suzuki, Y. (1992), "Development of ultrasonic axial bolting force inspection system for turbine bolts in thermal power plants", JSME Int. J. Ser. 1, Solid Mech. Strength Mater., 35(2), 216-219. https://doi.org/10.1299/jsmea1988.35.2_216
Torrey, L. and Shavlik, J. (2010), "Transfer learning", in Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, IGI Global, PA, USA.
Van Dyk, D.A. and Meng, X.-L. (2001), "The art of data augmentation", J. Comput. Graph. Stat., 10(1), 1-50. https://doi.org/10.1198/10618600152418584
Wang, T., Song, G., Liu, S., Li, Y. and Xiao, H. (2013a), "Review of bolted connection monitoring", Int. J. Distrib. Sens. Netw., 9(12), 871213. https://doi.org/10.1155/2013/871213

상세보기
Wang, T., Song, G., Wang, Z. and Li, Y. (2013b), "Proof-of-concept study of monitoring bolt connection status using a piezoelectric based active sensing method", Smart Mater. Struct., 22(8), 87001. https://doi.org/10.1088/0964-1726/22/8/087001

상세보기
Zhang, Y., Sun, X., Loh, K.J., Su, W., Xue, Z. and Zhao, X. (2019), "Autonomous bolt loosening detection using deep learning", Struct. Health Monit., 19(1), 105-122. https://doi.org/10.1177/1475921719837509

상세보기
Zhao, X., Zhang, Y. and Wang, N. (2019), "Bolt loosening angle detection technology using deep learning", Struct. Control Health Monit., 26(1), e2292. https://doi.org/10.1002/stc.2292

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