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Vision‐based automated bridge component recognition with high‐level scene consistency
Author(s) -
Narazaki Yasutaka,
Hoskere Vedhus,
Hoang Tu A.,
Fujino Yozo,
Sakurai Akito,
Spencer Billie F.
Publication year - 2020
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12505
Subject(s) - bridge (graph theory) , component (thermodynamics) , computer science , consistency (knowledge bases) , artificial intelligence , segmentation , pattern recognition (psychology) , class (philosophy) , computer vision , medicine , physics , thermodynamics
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.