Ice Load Measurements on Known Structures Using Image Processing Methods
This study employs a method based on image analyses and structure information to detect accumulated ice on known structures. The icing of marine vessels and offshore structures causes significant reductions in their efficiency and creates unsafe working conditions. Image processing methods are used to measure ice loads automatically. Most image processing methods are developed based on captured image analyses. In this method, ice loads on structures are calculated by defining structure coordinates and processing captured images. A pyramidal structure is designed with nine cylindrical bars as the known structure of experimental setup. Unsymmetrical ice accumulated on the structure in a cold room represents the actual case of experiments. Camera intrinsic and extrinsic parameters are used to define structure coordinates in the image coordinate system according to the camera location and angle. The thresholding method is applied to capture images and detect iced structures in a binary image. The ice thickness of each element is calculated by combining the information from the binary image and the structure coordinate. Averaging ice diameters from different camera views obtains ice thicknesses of structure elements. Comparison between ice load measurements using this method and the actual ice loads shows positive correlations with an acceptable range of error. The method can be applied to complex structures defining structure and camera coordinates.
 C. C. Ryerson, “Ice protection of offshore platforms,” Cold Reg. Sci. Technol., vol. 65, no. 1, pp. 97–110, 2011.
 A. Bodaghkhani, S.-R. Dehghani, Y. S. Muzychka, and B. Colbourne, “Understanding spray cloud formation by wave impact on marine objects,” Cold Reg. Sci. Technol., vol. 129, pp. 114–136, 2016.
 A. R. Dehghani-Sanij, S. R. Dehghani, G. F. Naterer, and Y. S. Muzychka, “Sea spray icing phenomena on marine vessels and offshore structures: review and formulation,” Ocean Eng., vol. 132, pp. 25–39, 2017.
 E. P. Lozowski, K. Szilder, and L. Makkonen, “Computer simulation of marine ice accretion,” Philos. Trans. R. Soc. London A Math. Phys. Eng. Sci., vol. 358, no. 1776, pp. 2811–2845, Nov. 2000.
 C. C. Ryerson, “Superstructure spray and ice accretion on a large U.S. Coast Guard cutter,” Atmos. Res., vol. 36, no. 3–4, pp. 321–337, 1995.
 C. C. Ryerson, “Assessment of Superstructure Ice Protection as Applied to Offshore Oil Operations Safety: Problems, Hazards, Needs, and Potential Transfer Technologies,” Erdc/Crrel Tr-08-14, no. September, p. 156, 2008.
 D. J. Palmer A., “Development of a Marine Icing Monitoring System,” Proc. 20th Int. Conf. Port Ocean Eng. under Arct. Cond., pp. 1–12, 2009.
 X. Wang, J. Hu, B. Wu, L. Du, and C. Sun, “Study on edge extraction methods for image-based icing on-line monitoring on overhead transmission lines,” 2008 Int. Conf. High Volt. Eng. Appl. ICHVE 2008, pp. 661–665, 2008.
 C. Yu, Q. Peng, R. Wachal, and P. Wang, “An image-based 3D acquisition of ice accretions on power transmission lines,” Can. Conf. Electr. Comput. Eng., no. May, pp. 2005–2008, 2007.
 A. Fazelpour, S. R. Dehghani, V. Masek, and Y. S. Muzychka, “Effect of Ambient Conditions on Infrared Ice Thickness Measurement,” in IEEE Newfoundland Section Conference, 2016.
 A. Fazelpour, S. R. Dehghani, V. Masek, and Y. S. Muzychka, “Infrared Image Analysis for Estimation of Ice Load on Structures,” in Arctic Technology Conference, 2016.
 E. R. Davies, Computer and Machine Vision: Theory. 2012.
 N. Otsu, “A threshold selection method from gray-level histograms,” Automatica, vol. 11, no. 285–296, pp. 23–27, 1975.
 R. C. S. Gonzalez and P. Wintz, “Digital image processing,” 1977.