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      Framework for Structural Health Monitoring of Steel Bridges by Computer Vision

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          Abstract

          The monitoring of a structural condition of steel bridges is an important issue. Good condition of infrastructure facilities ensures the safety and economic well-being of society. At the same time, due to the continuous development, rising wealth of the society and socio-economic integration of countries, the number of infrastructural objects is growing. Therefore, there is a need to introduce an easy-to-use and relatively low-cost method of bridge diagnostics. We can achieve these benefits by the use of Unmanned Aerial Vehicle-Based Remote Sensing and Digital Image Processing. In our study, we present a state-of-the-art framework for Structural Health Monitoring of steel bridges that involves literature review on steel bridges health monitoring, drone route planning, image acquisition, identification of visual markers that may indicate a poor condition of the structure and determining the scope of applicability. The presented framework of image processing procedure is suitable for diagnostics of steel truss riveted bridges. In our considerations, we used photographic documentation of the Fitzpatrick Bridge located in Tallassee, Alabama, USA.

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          Comparative study of Hough Transform methods for circle finding

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            A Vision-Based Sensor for Noncontact Structural Displacement Measurement

            Conventional displacement sensors have limitations in practical applications. This paper develops a vision sensor system for remote measurement of structural displacements. An advanced template matching algorithm, referred to as the upsampled cross correlation, is adopted and further developed into a software package for real-time displacement extraction from video images. By simply adjusting the upsampling factor, better subpixel resolution can be easily achieved to improve the measurement accuracy. The performance of the vision sensor is first evaluated through a laboratory shaking table test of a frame structure, in which the displacements at all the floors are measured by using one camera to track either high-contrast artificial targets or low-contrast natural targets on the structural surface such as bolts and nuts. Satisfactory agreements are observed between the displacements measured by the single camera and those measured by high-performance laser displacement sensors. Then field tests are carried out on a railway bridge and a pedestrian bridge, through which the accuracy of the vision sensor in both time and frequency domains is further confirmed in realistic field environments. Significant advantages of the noncontact vision sensor include its low cost, ease of operation, and flexibility to extract structural displacement at any point from a single measurement.
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              Size invariant circle detection

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                27 January 2020
                February 2020
                : 20
                : 3
                : 700
                Affiliations
                [1 ]Computer Science and Electrical Engineering, Faculty of Telecommunications, University of Science and Technology in Bydgoszcz, Al. prof. S. Kaliskiego 7, 85-796 Bydgoszcz, Poland; adimar@ 123456utp.edu.pl
                [2 ]Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdansk, Poland
                [3 ]Department of Civil Engineering, Auburn University, 261 W Magnolia Ave, Auburn, AL 36849, USA; vha0001@ 123456auburn.edu
                [4 ]Facultad de Ingeniería y Tecnología, Universidad San Sebastián, Lientur 1457, Concepción 4080871, Chile
                Author notes
                [* ]Correspondence: patziolk@ 123456pg.edu.pl ; Tel.: +48-58-347-2385
                Author information
                https://orcid.org/0000-0002-0651-3208
                https://orcid.org/0000-0001-8809-6702
                https://orcid.org/0000-0001-7509-9103
                Article
                sensors-20-00700
                10.3390/s20030700
                7039231
                32012791
                099b38f8-166f-4b45-b90c-93234ec41ffb
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 24 December 2019
                : 21 January 2020
                Categories
                Article

                Biomedical engineering
                computer vision,drones,image processing,steel structures,structural health monitoring

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