Vision‐based automated bridge component recognition with high‐level scene consistency
This research investigates vision‐based automated bridge component recognition, which is critical for automating visual inspection of bridges during initial response after earthquakes. Semantic segmentation algorithms with up to 45 convolutional layers are applied to recognize bridge components from...
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Veröffentlicht in: | Computer-aided civil and infrastructure engineering 2020-05, Vol.35 (5), p.465-482 |
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container_title | Computer-aided civil and infrastructure engineering |
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creator | Narazaki, Yasutaka Hoskere, Vedhus Hoang, Tu A. Fujino, Yozo Sakurai, Akito Spencer, Billie F. |
description | This research investigates vision‐based automated bridge component recognition, which is critical for automating visual inspection of bridges during initial response after earthquakes. Semantic segmentation algorithms with up to 45 convolutional layers are applied to recognize bridge components from images of complex scenes. One of the challenges in such scenarios is to get the recognition results consistent with high‐level scene structure using limited amount of training data. To impose the high‐level scene consistency, this research combines 10‐class scene classification and 5‐class bridge component classification. Three approaches are investigated to combine scene classification results into bridge component classification: (a) naïve configuration, (b) parallel configuration, and (c) sequential configuration of classifiers. The proposed approaches, sequential configuration in particular, are demonstrated to be effective in recognizing bridge components in complex scenes, showing less than 1% of accuracy loss from the naïve/parallel configuration for bridge images, and less than 1% false positives for the nonbridge images. |
doi_str_mv | 10.1111/mice.12505 |
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Semantic segmentation algorithms with up to 45 convolutional layers are applied to recognize bridge components from images of complex scenes. One of the challenges in such scenarios is to get the recognition results consistent with high‐level scene structure using limited amount of training data. To impose the high‐level scene consistency, this research combines 10‐class scene classification and 5‐class bridge component classification. Three approaches are investigated to combine scene classification results into bridge component classification: (a) naïve configuration, (b) parallel configuration, and (c) sequential configuration of classifiers. 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Semantic segmentation algorithms with up to 45 convolutional layers are applied to recognize bridge components from images of complex scenes. One of the challenges in such scenarios is to get the recognition results consistent with high‐level scene structure using limited amount of training data. To impose the high‐level scene consistency, this research combines 10‐class scene classification and 5‐class bridge component classification. Three approaches are investigated to combine scene classification results into bridge component classification: (a) naïve configuration, (b) parallel configuration, and (c) sequential configuration of classifiers. The proposed approaches, sequential configuration in particular, are demonstrated to be effective in recognizing bridge components in complex scenes, showing less than 1% of accuracy loss from the naïve/parallel configuration for bridge images, and less than 1% false positives for the nonbridge images.</description><subject>Algorithms</subject><subject>Automation</subject><subject>Bridge inspection</subject><subject>Classification</subject><subject>Configurations</subject><subject>Consistency</subject><subject>Image segmentation</subject><subject>Recognition</subject><subject>Seismic response</subject><subject>Vision</subject><issn>1093-9687</issn><issn>1467-8667</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kLFOwzAYhC0EEqWw8ASR2JBS7NhxnBFVBSoVsQASU-Q4v1tXiV3slKobj8Az8iS4hJlb7obv_l86hC4JnpCom84omJAsx_kRGhHGi1RwXhzHjEuallwUp-gshDWOYoyO0NurCcbZ78-vWgZoErntXSf7mGpvmiUkynUbZ8H2iQflltb0EU92pl8lK7NcxWILH9AmQYE90DaY0INV-3N0omUb4OLPx-jlbvY8fUgXT_fz6e0iVZSxPIVSU6IbCblkAI0kWcFAUYEpLrKMcyy0KOsSNxq4bEDopqw5gGCiELpkOR2jq-Huxrv3LYS-Wrutt_FllVHBBStycaCuB0p5F4IHXW286aTfVwRXh-mqw3TV73QRJgO8My3s_yGrx_l0NnR-AJurdLA</recordid><startdate>202005</startdate><enddate>202005</enddate><creator>Narazaki, Yasutaka</creator><creator>Hoskere, Vedhus</creator><creator>Hoang, Tu A.</creator><creator>Fujino, Yozo</creator><creator>Sakurai, Akito</creator><creator>Spencer, Billie F.</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>202005</creationdate><title>Vision‐based automated bridge component recognition with high‐level scene consistency</title><author>Narazaki, Yasutaka ; Hoskere, Vedhus ; Hoang, Tu A. ; Fujino, Yozo ; Sakurai, Akito ; Spencer, Billie F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3445-e9f31fdae5a4eeda1274ec380307226608f89b90dfe6ade8fd9b6ee84878f9453</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Automation</topic><topic>Bridge inspection</topic><topic>Classification</topic><topic>Configurations</topic><topic>Consistency</topic><topic>Image segmentation</topic><topic>Recognition</topic><topic>Seismic response</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Narazaki, Yasutaka</creatorcontrib><creatorcontrib>Hoskere, Vedhus</creatorcontrib><creatorcontrib>Hoang, Tu A.</creatorcontrib><creatorcontrib>Fujino, Yozo</creatorcontrib><creatorcontrib>Sakurai, Akito</creatorcontrib><creatorcontrib>Spencer, Billie F.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computer-aided civil and infrastructure engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Narazaki, Yasutaka</au><au>Hoskere, Vedhus</au><au>Hoang, Tu A.</au><au>Fujino, Yozo</au><au>Sakurai, Akito</au><au>Spencer, Billie F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Vision‐based automated bridge component recognition with high‐level scene consistency</atitle><jtitle>Computer-aided civil and infrastructure engineering</jtitle><date>2020-05</date><risdate>2020</risdate><volume>35</volume><issue>5</issue><spage>465</spage><epage>482</epage><pages>465-482</pages><issn>1093-9687</issn><eissn>1467-8667</eissn><abstract>This research investigates vision‐based automated bridge component recognition, which is critical for automating visual inspection of bridges during initial response after earthquakes. Semantic segmentation algorithms with up to 45 convolutional layers are applied to recognize bridge components from images of complex scenes. One of the challenges in such scenarios is to get the recognition results consistent with high‐level scene structure using limited amount of training data. To impose the high‐level scene consistency, this research combines 10‐class scene classification and 5‐class bridge component classification. Three approaches are investigated to combine scene classification results into bridge component classification: (a) naïve configuration, (b) parallel configuration, and (c) sequential configuration of classifiers. 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subjects | Algorithms Automation Bridge inspection Classification Configurations Consistency Image segmentation Recognition Seismic response Vision |
title | Vision‐based automated bridge component recognition with high‐level scene consistency |
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