Title: Real‐time generic target tracking for structural displacement monitoring under environmental uncertainties via deep learning
Abstract: Structural Control and Health MonitoringVolume 29, Issue 3 e2902 RESEARCH ARTICLE Real-time generic target tracking for structural displacement monitoring under environmental uncertainties via deep learning Jong-Hyun Jeong, Jong-Hyun Jeong orcid.org/0000-0001-9172-2524 Department of Civil & Architectural Engineering & Mechanics, The University of Arizona, Tucson, Arizona, USASearch for more papers by this authorHongki Jo, Corresponding Author Hongki Jo [email protected] orcid.org/0000-0001-5056-1154 Department of Civil & Architectural Engineering & Mechanics, The University of Arizona, Tucson, Arizona, USA Correspondence Hongki Jo, Department of Civil & Architectural Engineering & Mechanics, The University of Arizona, Tucson, AZ, USA. Email: [email protected]Search for more papers by this author Jong-Hyun Jeong, Jong-Hyun Jeong orcid.org/0000-0001-9172-2524 Department of Civil & Architectural Engineering & Mechanics, The University of Arizona, Tucson, Arizona, USASearch for more papers by this authorHongki Jo, Corresponding Author Hongki Jo [email protected] orcid.org/0000-0001-5056-1154 Department of Civil & Architectural Engineering & Mechanics, The University of Arizona, Tucson, Arizona, USA Correspondence Hongki Jo, Department of Civil & Architectural Engineering & Mechanics, The University of Arizona, Tucson, AZ, USA. Email: [email protected]Search for more papers by this author First published: 03 December 2021 https://doi.org/10.1002/stc.2902 Funding information: Salt River Project Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Summary While structural displacement provides essential information about static and/or low-frequency dynamic characteristics of structural behaviors, full-scale measurement of absolute displacement in field structures is extremely challenging because of the requirement of fixed reference in most cases. Recent computer vision-based sensing technologies have advanced to the level of reference-free monitoring of full-scale dynamic displacement using generic features of the structure. However, current generic feature-based methods have limited to only short-term or campaign-type monitoring applications due to the intrinsic limitations of computer-vision sensing under variable environmental conditions. This study investigates deep learning-based approaches for real-time computer-vision sensing that enables displacement monitoring using generic features under harsh environmental uncertainties. Distractor-Aware Siamese Region Proposal Network (DaSiamRPN) was employed to address the environmental uncertainty issues, particularly caused by luminous condition change and obstructed vision, without sacrificing real-time processing capability. A series of indoor and outdoor experiments have been conducted to evaluate the performance under light condition change, occlusion, and haze. Comparative tests showed that the proposed method outperformed other various vision-based object tracking methods, showing the feasibility for long-term structural displacement monitoring of full-scale structures. Open Research DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author, H. Jo, upon reasonable request. Volume29, Issue3March 2022e2902 RelatedInformation