Image-Based Automatic Bridge Component Classification Using Deep Learning

Cho, Munwon, Lee, Jae Hyuk, Ryu, Young-Moo, Park, Jeongjun, Yoon, Hyungchul

doi:10.12652/Ksce.2021.41.6.0751

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2021 Vol.41, Issue 6 Preview Page

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Image-Based Automatic Bridge Component Classification Using Deep Learning 딥러닝을 활용한 이미지 기반 교량 구성요소 자동분류 네트워크 개발

1 December 2021. pp. 751-760

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Abstract

우리나라의 교량은 대부분이 건설된 지 20년 이상이 지나 현재 노후화로 인하여 많은 문제점이 제기되고 있으며, 교량의 안전점검은 대부분 전문 인력의 주관적인 평가로 이루어지고 있다. 최근 교량 안전점검의 데이터의 체계적인 관리를 위해 BIM 등을 활용한 데이터 기반의 유지관리 기술들이 개발되고 있지만, BIM과 구조물의 유지관리 데이터를 연동을 위해서 영상정보를 직접 라벨링하는 수작업을 필요로한다. 따라서 본 논문에서는 이미지 기반의 자동 교량 구성요소 분류 네트워크를 개발하고자 한다. 본 연구에서 제안한 방법은 두 개의 CNN 네트워크로 구성되었다. 첫 번째 네트워크에서 특정 교량 이미지에 대하여 교량의 형식을 자동으로 분류한 뒤, 두 번째 네트워크에서 교량의 형식별로 구성요소를 분류함으로써 정확도와 효율성을 향상시키고자 한다. 본 연구에서 개발한 시스템을 검증한 결과, 847개의 교량 이미지에 대해서 98.1 %의 정확도로 교량의 구성요소를 자동으로 분류 할 수 있었다. 본 연구에서 개발한 교량의 구성요소 자동분류 기술은 향후 교량의 유지관리에 기여를 할 수 있을 것으로 기대된다.

키워드

BIM

딥러닝

CNN

교량 구성요소 분류

Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice.

Keywords

BIM

Deep learning

CNN

Bridge component classification

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Information

Publisher :Korean Society of Civil Engineers
Publisher(Ko) :대한토목학회
Journal Title :KSCE JOURNAL OF CIVIL AND ENVIRONMENTAL ENGINEERING RESEARCH
Journal Title(Ko) :대한토목학회 논문집
Volume : 41
No :6
Pages :751-760
Received Date :2021. 06. 03
Revised Date :2021. 08. 05
Accepted Date : 2021. 09. 23
DOI :https://doi.org/10.12652/Ksce.2021.41.6.0751

KSCE JOURNAL OF CIVIL AND ENVIRONMENTAL ENGINEERING RESEARCH ISSN:1015-6348(Print) 2287-934X(Online) 대한토목학회 논문집

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