Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김은주 | - |
dc.date.accessioned | 2022-11-14T23:52:26Z | - |
dc.date.available | 2022-11-14T23:52:26Z | - |
dc.date.issued | 2021-08 | - |
dc.identifier.citation | 한국도로학회논문집, v. 23, NO. 4, Page. 75-81 | en_US |
dc.identifier.issn | 1738-7159;2287-3678 | en_US |
dc.identifier.uri | http://www.ijhe.or.kr/journal/article.php?code=80067 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/176770 | - |
dc.description.abstract | PURPOSES : This study aims to develop and evaluate computer vision-based algorithms that classify the road roughness index (IRI) of road specimens with known IRIs. The presented study develops and compares classifier-based and deep learning-based models that can effectively determine pavement roughness grades. METHODS : A set road specimen was developed for various IRIs by generating road profiles with matching standard deviations. In addition, five distinct features from road images, including mean, peak-to-peak, standard variation, and mean absolute deviation, were extracted to develop a classifier-based model. From parametric studies, a support vector machine (SVM) was selected. To further demonstrate that the model is more applicable to real-world problems, with a non-integer road grade, a deep-learning model was developed. The algorithm was proposed by modifying the MNIST database, and the model input parameters were determined to achieve higher precision. RESULTS : The results of the proposed algorithms indicated the potential of using computer vision-based models for classifying road surface roughness. When SVM was adopted, near 100% precision was achieved for the training data, and 98% for the test data. Although the model indicated accurate results, the model was classified based on integer IRIs, which is less practical. Alternatively, a deep-learning model, which can be applied to a non-integer road grade, indicated an accuracy of over 85%. CONCLUSIONS : In this study, both the classifier-based, and deep-learning-based models indicated high precision for estimating road surface roughness grades. However, because the proposed algorithm has only been verified against the road model with fixed integers, optimization and verification of the proposed algorithm need to be performed for a real road condition. | en_US |
dc.description.sponsorship | 본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었습니다(과제번호: 21CTAP-C164093-01). 연구지원에 감사드립니다. | en_US |
dc.language | ko | en_US |
dc.publisher | 한국도로학회 | en_US |
dc.subject | IRI | en_US |
dc.subject | machine learning | en_US |
dc.subject | MNIST model | en_US |
dc.subject | road surface roughness | en_US |
dc.subject | SVM | en_US |
dc.subject | vision-based road assessment | en_US |
dc.title | 이미지기반 도로 노면 평탄성 예측을 위한 머신러닝 모델 개발 | en_US |
dc.title.alternative | A Computer-vision-based classification of road surface roughness grade using Machine Learning Techniques | en_US |
dc.type | Article | en_US |
dc.relation.no | 4 | - |
dc.relation.volume | 23 | - |
dc.identifier.doi | 10.7855/IJHE.2021.23.4.075 | en_US |
dc.relation.page | 75-81 | - |
dc.relation.journal | 한국도로학회논문집 | - |
dc.contributor.googleauthor | 이영재 | - |
dc.contributor.googleauthor | 전성일 | - |
dc.contributor.googleauthor | 김은주 | - |
dc.sector.campus | S | - |
dc.sector.daehak | 공과대학 | - |
dc.sector.department | 건설환경공학과 | - |
dc.identifier.pid | robinekim | - |
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