{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T05:42:19Z","timestamp":1778391739310,"version":"3.51.4"},"reference-count":50,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"US Department of Transportation Accelerated Bridge Construction University Transportation Center","award":["(ABC-UTC) 2016"],"award-info":[{"award-number":["(ABC-UTC) 2016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Computer vision in the structural health monitoring (SHM) field has become popular, especially for processing unmanned aerial vehicle (UAV) data, but still has limitations both in experimental testing and in practical applications. Prior works have focused on UAV challenges and opportunities for the vibration-based SHM of buildings or bridges, but practical and methodological gaps exist specifically for linear infrastructure systems such as pipelines. Since they are critical for the transportation of products and the transmission of energy, a feasibility study of UAV-based SHM for linear infrastructures is essential to ensuring their service continuity through an advanced SHM system. Thus, this study proposes a single UAV for the seismic monitoring and safety assessment of linear infrastructures along with their computer vision-aided procedures. The proposed procedures were implemented in a full-scale shake-table test of a natural gas pipeline assembly. The objectives were to explore the UAV potential for the seismic vibration monitoring of linear infrastructures with the aid of several computer vision algorithms and to investigate the impact of parameter selection for each algorithm on the matching accuracy. The procedure starts by adopting the Maximally Stable Extremal Region (MSER) method to extract covariant regions that remain similar through a certain threshold of image series. The feature of interest is then detected, extracted, and matched using the Speeded-Up Robust Features (SURF) and K-nearest Neighbor (KNN) algorithms. The Maximum Sample Consensus (MSAC) algorithm is applied for model fitting by maximizing the likelihood of the solution. The output of each algorithm is examined for correctness in matching pairs and accuracy, which is a highlight of this procedure, as no studies have ever investigated these properties. The raw data are corrected and scaled to generate displacement data. Finally, a structural safety assessment was performed using several system identification models. These procedures were first validated using an aluminum bar placed on an actuator and tested in three harmonic tests, and then an implementation case study on the pipeline shake-table tests was analyzed. The validation tests show good agreement between the UAV data and reference data. The shake-table test results also generate reasonable seismic performance and assess the pipeline seismic safety, demonstrating the feasibility of the proposed procedure and the prospect of UAV-based SHM for linear infrastructure monitoring.<\/jats:p>","DOI":"10.3390\/s24051450","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T07:33:56Z","timestamp":1708673636000},"page":"1450","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3732-7210","authenticated-orcid":false,"given":"Luna","family":"Ngeljaratan","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA"},{"name":"Research Center for Structural Strength Technology, National Research and Innovation Agency (BRIN), Science and Technology Research Center Bd. 220, Setu, Tangerang Selatan 15314, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9064-5413","authenticated-orcid":false,"given":"Elif Ecem","family":"Bas","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA"},{"name":"R&D Test Systems A\/S, 8382 Hinnerup, Denmark"}]},{"given":"Mohamed A.","family":"Moustafa","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA"},{"name":"New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100274","DOI":"10.1016\/j.trgeo.2019.100274","article-title":"Effect of seismic and soil parameter uncertainties on seismic damage of buried segmented pipeline","volume":"21","author":"Wijaya","year":"2019","journal-title":"Transp. Geotech."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1139\/l95-052","article-title":"Performance of lifelines during the 1994 Northridge earthquake","volume":"22","author":"Lau","year":"1995","journal-title":"Can. J. Civ. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"04018083","DOI":"10.1061\/(ASCE)CF.1943-5509.0001214","article-title":"Review of pipeline performance during earthquakes since 1906","volume":"32","author":"Nair","year":"2018","journal-title":"J. Perform. Constr. Facil."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Folga, S.M. Natural gas pipeline technology overview, Argonne National Laboratory ANL\/EVS\/TM\/08-5, 2007.","DOI":"10.2172\/925391"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1109\/JSEN.2011.2181161","article-title":"State-of-the-art review of technologies for pipe structural health monitoring","volume":"12","author":"Liu","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"104226","DOI":"10.1016\/j.autcon.2022.104226","article-title":"Acoustic leak detection approaches for water pipelines","volume":"138","author":"Fan","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Chen, D., Zhang, H., Giraud, F., and Paik, J. (2020). Multimodal pipe-climbing robot with origami clutches and soft modular legs. Bioinspiration Biomim., 15.","DOI":"10.1088\/1748-3190\/ab5928"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102321","DOI":"10.1016\/j.apor.2020.102321","article-title":"Subsea pipeline leak inspection by autonomous underwater vehicle","volume":"107","author":"Zhang","year":"2021","journal-title":"Appl. Ocean. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"109633","DOI":"10.1016\/j.petrol.2021.109633","article-title":"UAV-based remote sensing for the petroleum industry and environmental monitoring: State-of-the-art and perspectives","volume":"208","author":"Asadzadeh","year":"2022","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"L\u00f3pez, F., and Maldague, X. (2021). A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis. Appl. Sci., 11.","DOI":"10.3390\/app11041412"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104273","DOI":"10.1016\/j.autcon.2022.104273","article-title":"Intelligent robotic systems for structural health monitoring: Applications and future trends","volume":"139","author":"Tian","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Freeman, M., Vernon, C., Berrett, B., Hastings, N., Derricott, J., Pace, J., Horne, B., Hammond, J., Janson, J., and Chiabrando, F. (2019). Sequential earthquake damage assessment incorporating optimized sUAV Remote Sensing at Pescara del Tronto. Geosciences, 9.","DOI":"10.3390\/geosciences9080332"},{"key":"ref_13","unstructured":"Sutton, M.A., Orteu, J.J., and Schreier, H. (2009). Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications, Springer Science & Business Media."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s11340-021-00710-z","article-title":"Drone-based StereoDIC: Experimental validation and infrastructure application","volume":"61","author":"Kalaitzakis","year":"2021","journal-title":"Exp. Mech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Van Gool, L. (2006). SURF: Speeded Up Robust Features, Springer.","DOI":"10.1007\/11744023_32"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/TRO.2017.2705103","article-title":"Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras","volume":"33","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1016\/S0262-8856(97)00010-3","article-title":"Robust parameterization and computation of the trifocal tensor","volume":"15","author":"Torr","year":"1997","journal-title":"Image Vis. Comput."},{"key":"ref_19","unstructured":"Chum, O., and Matas, J. (2005, January 20\u201326). Matching with PROSAC-progressive sample consensus. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1006\/cviu.1999.0832","article-title":"MLESAC: A new robust estimator with application to estimating image geometry","volume":"78","author":"Torr","year":"2000","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Otzeta, J.M., Rodr\u00edguez-Moreno, I., Mendialdua, I., and Sierra, B. (2022). Ransac for robotic applications: A survey. Sensors, 23.","DOI":"10.3390\/s23010327"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bendris, B., and Becerra, J.C. (2022). Design and experimental evaluation of an aerial solution for visual inspection of tunnel-like infrastructures. Remote Sens., 14.","DOI":"10.3390\/rs14010195"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wu, Y., Qin, Y., Wang, Z., and Jia, L. (2018). A UAV-based visual inspection method for rail surface defects. Appl. Sci., 8.","DOI":"10.3390\/app8071028"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yuan, X., Li, W., and Chen, S. (2017). Automatic power line inspection using UAV images. Remote Sens., 9.","DOI":"10.3390\/rs9080824"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhu, C., Zhu, J., Bu, T., and Gao, X. (2022). Monitoring and Identification of Road Construction Safety Factors via UAV. Sensors, 22.","DOI":"10.3390\/s22228797"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"351","DOI":"10.3319\/TAO.2018.12.09.02","article-title":"Mapping surface breakages of the 2018 Hualien earthquake by using UAS photogrammetry","volume":"30","author":"Lin","year":"2019","journal-title":"Terr. Atmos. Ocean. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Matsuoka, K., Uehan, F., Kusaka, H., and Tomonaga, H. (2021). Experimental validation of Non-Marker simple image displacement measurements for railway bridges. Appl. Sci., 11.","DOI":"10.3390\/app11157032"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e2862","DOI":"10.1002\/stc.2862","article-title":"Monitoring the earthquake response of full-scale structures using UAV vision-based techniques","volume":"29","author":"Wang","year":"2022","journal-title":"Struct. Control. Health Monit."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e3025","DOI":"10.1002\/stc.3025","article-title":"Vision-based displacement measurement using an unmanned aerial vehicle","volume":"29","author":"Han","year":"2022","journal-title":"Struct. Control. Health Monit."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.autcon.2018.06.015","article-title":"Feasibility study for drone-based masonry construction of real-scale structures","volume":"94","author":"Goessens","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e04994","DOI":"10.1016\/j.heliyon.2020.e04994","article-title":"Post-movement stabilization time for the downwash region of a 6-rotor UAV for remote gas monitoring","volume":"6","author":"Brinkman","year":"2020","journal-title":"Heliyon"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107869","DOI":"10.1016\/j.ymssp.2021.107869","article-title":"Non-contact structural displacement measurement using unmanned aerial vehicles and video-based systems","volume":"160","author":"Ribeiro","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wu, Z., Chen, G., Ding, Q., Yuan, B., and Yang, X. (2021). Three-dimensional reconstruction-based vibration measurement of bridge model using UAVs. Appl. Sci., 11.","DOI":"10.3390\/app11115111"},{"key":"ref_34","first-page":"14","article-title":"3-D Vermessung von Oberfl\u00e4chen und Bauteilen durch Photogrammetrie und Bildverarbeitung","volume":"91","author":"Schneider","year":"1991","journal-title":"Proc. Ident\/Vis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1111\/mice.12653","article-title":"A compressive sensing method for processing and improving vision-based target-tracking signals for structural health monitoring","volume":"36","author":"Ngeljaratan","year":"2021","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ngeljaratan, L., and Moustafa, M.A. (2020). Implementation and evaluation of vision-based sensor image compression for close-range photogrammetry and structural health monitoring. Sensors, 20.","DOI":"10.3390\/s20236844"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"110551","DOI":"10.1016\/j.engstruct.2020.110551","article-title":"Structural health monitoring and seismic response assessment of bridge structures using target-tracking digital image correlation","volume":"213","author":"Ngeljaratan","year":"2020","journal-title":"Eng. Struct."},{"key":"ref_38","first-page":"75","article-title":"Uncertainty and accuracy of vision-based tracking concerning stereophotogrammetry and noise-floor tests","volume":"29","author":"Ngeljaratan","year":"2022","journal-title":"Metrol. Meas. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ngeljaratan, L., and Moustafa, M.A. (2021). Underexposed Vision-Based Sensors\u2019 Image Enhancement for Feature Identification in Close-Range Photogrammetry and Structural Health Monitoring. Appl. Sci., 11.","DOI":"10.3390\/app112311086"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1049\/iet-ipr.2015.0150","article-title":"Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement","volume":"9","author":"Lidong","year":"2015","journal-title":"IET Image Process."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.imavis.2004.02.006","article-title":"Robust wide-baseline stereo from maximally stable extremal regions","volume":"22","author":"Matas","year":"2004","journal-title":"Image Vis. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s11263-005-3848-x","article-title":"A comparison of affine region detectors","volume":"65","author":"Mikolajczyk","year":"2005","journal-title":"Int. J. Comput. Vis."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Salahat, E., Saleh, H., Sluzek, A., Al-Qutayri, M., Mohammad, B., and Ismail, M. (2015, January 9\u201312). A maximally stable extremal regions system-on-chip for real-time visual surveillance. Proceedings of the IECON 2015-41st Annual Conference of the IEEE Industrial Electronics Society, Yokohama, Japan.","DOI":"10.1109\/IECON.2015.7392528"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kristensen, F., and MacLean, W.J. (2007, January 27\u201330). Real-time extraction of maximally stable extremal regions on an FPGA. Proceedings of the 2007 IEEE International Symposium on Circuits and Systems, New Orleans, LA, USA.","DOI":"10.1109\/ISCAS.2007.378247"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"153","DOI":"10.5573\/IEIESPC.2016.5.3.153","article-title":"Recent advances in feature detectors and descriptors: A survey","volume":"5","author":"Lee","year":"2016","journal-title":"IEIE Trans. Smart Process. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"176","DOI":"10.5201\/ipol.2015.69","article-title":"An analysis of the SURF method","volume":"5","author":"Oyallon","year":"2015","journal-title":"Image Process. Line"},{"key":"ref_47","first-page":"1397","article-title":"Image stitching based on ORB feature and RANSAC","volume":"7","author":"Wu","year":"2016","journal-title":"Icic Express Lett. Part B Appl."},{"key":"ref_48","first-page":"1776","article-title":"Exploratory Study of Drone Data Stabilization with Implications in Vibration-based Structural Health Monitoring","volume":"10","author":"Ngeljaratan","year":"2023","journal-title":"Evergr. Jt. J. Nov. Carbon Resour. Sci. Green Asia Strategy"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1115\/1.1410370","article-title":"Stochastic system identification for operational modal analysis: A review","volume":"123","author":"Peeters","year":"2001","journal-title":"J. Dyn. Sys. Meas. Control"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3389\/fbuil.2019.00085","article-title":"System identification of large-scale bridges using target-tracking digital image correlation","volume":"5","author":"Ngeljaratan","year":"2019","journal-title":"Front. Built Environ."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1450\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:03:51Z","timestamp":1760105031000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1450"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,23]]},"references-count":50,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["s24051450"],"URL":"https:\/\/doi.org\/10.3390\/s24051450","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,23]]}}}