Fully automated vision-based loosened bolt detection using the Viola

Abstract

Many damage detection methods that use data obtained from contact sensors physically attached to structures have been developed. However, damage-sensitive features such as the modal properties of steel and reinforced concrete are sensitive to environmental conditions such as temperature and humidity. These uncertainties are difficult to address with a regression model or any other temperature compensation method, and these uncertainties are the primary causes of false alarms. A vision-based remote sensing system can be an option for addressing some of the challenges inherent in traditional sensing systems because it provides information about structural conditions. Using bolted connections is a common engineering practice, but very few vision-based techniques have been developed for loosened bolt detection. Thus, this article proposes a fully automated vision-based method for detecting loosened civil structural bolts using the Viola–Jones algorithm and support vector machines. Images of bolt connections for training were taken with a smartphone camera. The Viola–Jones algorithm was trained on two datasets of images with and without bolts to localize all the bolts in the images. The localized bolts were automatically cropped and binarized to calculate the bolt head dimensions and the exposed shank length. The calculated features were fed into a support vector machine to generate a decision boundary separating loosened and tight bolts. We tested our method on images taken with a digital single-lens reflex camera.

Keywords

Computer vision automatic detection damage detection loosened bolt support vector machines

Get full access to this article

View all access options for this article.

References

American Society of Civil Engineers (ASCE). 2013 report card for American infrastructure, http://2013.infrastructurereportcard.org (2013, accessed 19 February 2017).

Cha

Choi

Buyukozturk

Deep learning-based crack damage detection using convolutional neural networks. Comput-Aided Civ Inf 2017; 32(5): 361–378.

Salawu

OS.

Detection of structural damage through changes in frequency: a review. Eng Struct 1997; 19(9): 718–723.

Wang

Song

Liu

et al . Review of bolted connection monitoring. Int J Distrib Sens N 2013; 2013: 871213.

Epp

Cha

YJ.

Air-coupled impact-echo damage detection in reinforced concrete using wavelet transforms. Smart Mater Struct 2017; 26(2): 025018.

Cha

Trocha

Büyüköztürk

Field measurement-based system identification and dynamic response prediction of a unique MIT building. Sensors 2016; 16(7): E1016.

Cha

Wang

Unsupervised novelty detection–based structural damage localization using a density peaks-based fast clustering algorithm. Struct Health Monit. Epub ahead of print 13 February 2017. DOI: 10.1177/1475921717691260.

Doyle

Zagrai

Arritt

et al . Damage detection in bolted space structures. J Intel Mat Syst Str 2010; 21(3): 251–264.

Chen

Bolted joint looseness damage detection using electromechanical impedance measurements by PZT sensors. In: Proceedings of the 3rd international conference on smart materials and nanotechnology in engineering, Shenzhen, China, 5–8 December 2011, vol. 8409. Bellingham, WA: SPIE.

10.

Sevillano

Sun

Perera

Damage detection based on power dissipation measured with PZT sensors through the combination of electro-mechanical impedances and guided waves. Sensors 2016; 16(5): 639.

11.

Huda

Kajiwara

Hosoya

et al . Bolt loosening analysis and diagnosis by non-contact laser excitation vibration tests. Mech Syst Signal Pr 2013; 40(2): 589–604.

12.

Xia

Chen

Weng

et al . Temperature effect on vibration properties of civil structures: a literature review and case studies. J Civ Struct Healt Monit 2012; 2(1): 29–46.

13.

Deng

Xie

Damage detection with streamlined structural health monitoring data. Sensors 2015; 15(4): 8832–8851.

14.

Park

Kim

. Image-based bolt-loosening detection technique of bolt joint in steel bridges. In: Proceedings of the 6th international conference on advances in experimental structural engineering (AESE), University of Illinois at Urbana–Champaign, Urbana, IL, 1–2 August 2015.

15.

Cha

You

Choi

Vision-based detection of loosened bolts using the Hough transform and support vector machines. Automat Constr 2016; 71(2): 181–188.

16.

Cha

Chen

Büyüköztürk

Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters. Eng Struct 2017; 132: 300–313.

17.

Cha

Chen

Buyukozturk

Motion magnification based damage detection using high speed video. In: Proceedings of the 10th international workshop on structural health monitoring (IWSHM), Stanford University, Stanford, CA, 1–3 September 2015, pp. 1–3. USA: DEStech Publications, Inc.

18.

Yang

Nagarajaiah

Output-only modal identification by compressed sensing: non-uniform low-rate random sampling. Mech Syst Signal Pr 2015; 56–57: 15–34.

19.

Ramana

Choi

Cha

. Automated vision-based loosened bolt detection using the cascade detector. In: Proceedings of the 35th IMAC conference on structural dynamics, Garden Grove, CA, 30 January–2 February 2017, vol. 5, pp. 23–28. Cham: Springer.

20.

Viola

Jones

MJ.

Robust real-time face detection. Int J Comput Vision 2004; 57(2): 137–154.

21.

Gunn

SR.

Support vector machines for classification and regression. Report, University of Southampton, Southampton, May 1998.

22.

Dalal

Triggs

. Histograms of oriented gradients for human detection. In: Proceedings of the IEEE computer science conference on computer vision and pattern recognition, San Diego, CA, 20–25 June 2005, vol. 1, pp. 886–893. New York: IEEE.

23.

Kobayashi

Hidaka

Kurita

. Selection of histograms of oriented gradients features for pedestrian detection. In: Proceedings of the international conference on neural information processing, Kitakyushu, Japan, 13–16 November 2007, vol. 4985, pp. 598–607. Berlin: Springer.

24.

Viola

Jones

. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE computer science conference on computer vision and pattern recognition (CVPR 2001), Kauai, HI, 8–14 December 2001. Piscataway, NJ: IEEE.

25.

Cui

Cai

et al . License plate detection algorithm based on gentle AdaBoost algorithm with a cascade structure. In: Proceedings of the IEEE international conference on robotics and biomimetics (ROBIO), Guilin, China, 19–23 December 2009, pp. 1962–1966. New York: IEEE.

26.

Hamdi

Auhmani

Hassani

et al . An efficient gentle AdaBoost-based approach for mammograms classification. J Theor Appl Inform Tech 2015; 81(1): 138–143.

27.

Mathworks (2017a). Computer vision system toolbox (2017a): Train a cascade object detector, https://www.mathworks.com/help/vision/ug/train-a-cascade-object-detector.html, (accessed September 2016).

28.

Schapire

RE.

Explaining AdaBoost. In: Schölkopf

Luo

Vovk

(eds) Empirical interference. Berlin: Springer, 2013, pp. 37–52.

29.

Heydarzadeh

Haghighat

AT.

An efficient face detection method using AdaBoost and facial parts. Int J Simulat Syst Sci Tech 1997; 12(4): 1–9.

30.

Basca

Talos

Brad

. Randomized Hough transform for ellipse detection with result clustering. In: Proceedings of the international conference on computer as a tool (EUROCON 2005), Belgrade, 21–24 November 2005, vol. 2, pp. 1397–1400. New York: IEEE.

31.

Yuen

Princen

Illingworth

et al . Comparative study of Hough transform methods for circle finding. Image Vision Comput 1990; 8(1): 71–77.

32.

Al-Ameen

Sulong

Johar

MG.

Employing a suitable contrast enhancement technique as a pre-restoration adjustment phase for computed tomography medical images. Int J Biosci Biotechnol 2013; 5(1): 73–80.

33.

Mathworks (2017a). Computer vision system toolbox (2017a): activecontour, https://www.mathworks.com/help/images/ref/activecontour.html (accessed in November 2016).

Fully automated vision-based loosened bolt detection using the Viola–Jones algorithm

Abstract

Keywords

Get full access to this article

References