Complex image background segmentation for cable force estimation of urban bridges with drone‐captured video and deep learning

Structural Control and Health MonitoringVolume 29, Issue 4 e2910 RESEARCH ARTICLE Complex image background segmentation for cable force estimation of urban bridges with drone-captured video and deep learning Cheng Zhang, Cheng Zhang orcid.org/0000-0001-9646-937X School of Civil Engineering, Southeast University, Nanjing, ChinaSearch for more papers by this authorYongding Tian, Yongding Tian orcid.org/0000-0002-6320-3388 School of Civil Engineering, Southeast University, Nanjing, China School of Civil Engineering, Southwest Jiaotong University, Chengdu, ChinaSearch for more papers by this authorJian Zhang, Corresponding Author Jian Zhang [email protected] orcid.org/0000-0002-9129-255X School of Civil Engineering, Southeast University, Nanjing, China Correspondence Jian Zhang, School of Civil Engineering, Southeast University, 210096 Nanjing, China. Email: [email protected]Search for more papers by this author Cheng Zhang, Cheng Zhang orcid.org/0000-0001-9646-937X School of Civil Engineering, Southeast University, Nanjing, ChinaSearch for more papers by this authorYongding Tian, Yongding Tian orcid.org/0000-0002-6320-3388 School of Civil Engineering, Southeast University, Nanjing, China School of Civil Engineering, Southwest Jiaotong University, Chengdu, ChinaSearch for more papers by this authorJian Zhang, Corresponding Author Jian Zhang [email protected] orcid.org/0000-0002-9129-255X School of Civil Engineering, Southeast University, Nanjing, China Correspondence Jian Zhang, School of Civil Engineering, Southeast University, 210096 Nanjing, China. Email: [email protected]Search for more papers by this author First published: 08 December 2021 https://doi.org/10.1002/stc.2910 Funding information: Key R&D Program of Jiangsu Province, Grant/Award Number: BE2020094; National Science Foundation of China, Grant/Award Number: 51778134; National Key Research and Development Program of China, Grant/Award Number: 2019YFC1511105 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 Drone-assisted structural health monitoring has aroused extensive attention due to its high mobility and low cost. However, drone motion and complex image backgrounds severely impede its application in the cable force measurement of urban bridges. To fill this research gap, this paper proposed a deep learning-based complex background segmentation approach for cable force estimation of urban bridges from the drone-captured video. The main contribution of this article includes two aspects: (1) A pre-trained fully convolutional network (FCN) model was first adopted to identify bridge cables from drone-captured video and further to extract sub-pixel-level displacement using line segment detection (LSD) algorithm, and (2) an empirical mode decomposition algorithm was employed for extracting the vibration signal of bridge cables by eliminating the effect of drone motion on measured dynamic displacement. Finally, natural frequencies of bridge cables were obtained by performing Fourier analysis on extracted cable vibration and further adopted for cable force estimation. The effectiveness and robustness of the proposed method have been successfully verified by field testing of an urban cable-stayed footbridge. Estimated cable forces using the proposed method are consistent with the traditional contact-type measurements and design values, demonstrating the potential of this method for applying into rapid cable force estimation of numerous urban bridges. Volume29, Issue4April 2022e2910 RelatedInformation