@article{dda0fd979efe46dd8b2bb08758e42097,
title = "Vision-based automated bridge component recognition with high-level scene consistency",
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) na{\"i}ve 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 na{\"i}ve/parallel configuration for bridge images, and less than 1% false positives for the nonbridge images.",
author = "Yasutaka Narazaki and Vedhus Hoskere and Hoang, {Tu A.} and Yozo Fujino and Akito Sakurai and Spencer, {Billie F.}",
note = "Funding Information: The authors would like to acknowledge the financial support by the U.S. Army Corps of Engineers (Contract/Purchase Order No. W81EWF51409869). The authors would like to express their sincere gratitude to Hao Zhou, Siang Zhou, Xintao Wang, Peisong Wu, Xu Chen, Guangpan Zhou, Fernando Gomez, Li Zhu, Xinxia Li, Dongyu Zhang, and Yuguang Fu from the Smart Structures Technology Laboratory at University of Illinois at Urbana-Champaign, and Takahiro Yamaguchi from the Bridge Laboratory at the University of Tokyo, for labeling the image data. The authors would also like to thank Shintaro Arai from JFE Engineering Corporation for providing image data of bridges. Funding Information: The authors would like to acknowledge the financial support by the U.S. Army Corps of Engineers (Contract/Purchase Order No. W81EWF51409869). The authors would like to express their sincere gratitude to Hao Zhou, Siang Zhou, Xintao Wang, Peisong Wu, Xu Chen, Guangpan Zhou, Fernando Gomez, Li Zhu, Xinxia Li, Dongyu Zhang, and Yuguang Fu from the Smart Structures Technology Laboratory at University of Illinois at Urbana‐Champaign, and Takahiro Yamaguchi from the Bridge Laboratory at the University of Tokyo, for labeling the image data. The authors would also like to thank Shintaro Arai from JFE Engineering Corporation for providing image data of bridges. Publisher Copyright: {\textcopyright} 2019 Computer-Aided Civil and Infrastructure Engineering",
year = "2020",
month = may,
day = "1",
doi = "10.1111/mice.12505",
language = "English (US)",
volume = "35",
pages = "465--482",
journal = "Computer-Aided Civil and Infrastructure Engineering",
issn = "1093-9687",
publisher = "Wiley-Blackwell",
number = "5",
}