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Nondestructive Testing of Bridge Stay Cable Surface Defects Based on Computer Vision

Fengyu Xu1,2, Masoud Kalantari3, Bangjian Li2, Xingsong Wang2,*

1 Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot), College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
2 School of Mechanical Engineering, Southeast University Nanjing, 210096, China
3 Rubic Robotics Company, Alberta, Canada

* Corresponding Author: Xingsong Wang. Email: email

Computers, Materials & Continua 2023, 75(1), 2209-2226. https://doi.org/10.32604/cmc.2023.027102

Abstract

The automatically defect detection method using vision inspection is a promising direction. In this paper, an efficient defect detection method for detecting surface damage to cables on a cable-stayed bridge automatically is developed. A mechanism design method for the protective layer of cables of a bridge based on vision inspection and diameter measurement is proposed by combining computer vision and diameter measurement techniques. A detection system for the surface damages of cables is de-signed. Images of cable surfaces are then enhanced and subjected to threshold segmentation by utilizing the improved local grey contrast enhancement method and the improved maximum correlation method. Afterwards, the data obtained through diameter measurement are mined by employing the moving average method. Image enhancement, threshold segmentation, and diameter measurement methods are separately validated experimentally. The experimental test results show that the system delivers recall ratios for type-I and II surface defects of cables reaching 80.4% and 85.2% respectively, which accurately detects bulges on cable surfaces.

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APA Style
Xu, F., Kalantari, M., Li, B., Wang, X. (2023). Nondestructive testing of bridge stay cable surface defects based on computer vision. Computers, Materials & Continua, 75(1), 2209-2226. https://doi.org/10.32604/cmc.2023.027102
Vancouver Style
Xu F, Kalantari M, Li B, Wang X. Nondestructive testing of bridge stay cable surface defects based on computer vision. Comput Mater Contin. 2023;75(1):2209-2226 https://doi.org/10.32604/cmc.2023.027102
IEEE Style
F. Xu, M. Kalantari, B. Li, and X. Wang "Nondestructive Testing of Bridge Stay Cable Surface Defects Based on Computer Vision," Comput. Mater. Contin., vol. 75, no. 1, pp. 2209-2226. 2023. https://doi.org/10.32604/cmc.2023.027102



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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