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Convolutional sparse coding-based deep random vector functional link network for distress classification of road structures

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/79011

Title: Convolutional sparse coding-based deep random vector functional link network for distress classification of road structures
Authors: Maeda, Keisuke Browse this author
Takahashi, Sho Browse this author →KAKEN DB
Ogawa, Takahiro Browse this author →KAKEN DB
Haseyama, Miki Browse this author →KAKEN DB
Issue Date: Aug-2019
Publisher: John Wiley & Sons
Journal Title: Computer-aided civil and infrastructure engineering
Volume: 34
Issue: 8
Start Page: 654
End Page: 676
Publisher DOI: 10.1111/mice.12451
Abstract: This paper presents a convolutional sparse coding (CSC)-based deep random vector functional link network (CSDRN) for distress classification of road structures. The main contribution of this paper is the introduction of CSC into a feature extraction scheme in the distress classification. CSC can extract visual features representing characteristics of target images because it can successfully estimate optimal convolutional dictionary filters and sparse features as visual features by training from a small number of distress images. The optimal dictionaries trained from distress images have basic components of visual characteristics such as edge and line information of distress images. Furthermore, sparse feature maps estimated on the basis of the dictionaries represent both strength of the basic components and location information of regions having their components, and these maps can represent distress images. That is, sparse feature maps can extract key components from distress images that have diverse visual characteristics. Therefore, CSC-based feature extraction is effective for training from a limited number of distress images that have diverse visual characteristics. The construction of a novel neural network, CSDRN, by the use of a combination of CSC-based feature extraction and the DRN classifier, which can also be trained from a small dataset, is shown in this paper. Accurate distress classification is realized via the CSDRN.
Rights: This is the peer reviewed version of the following article: Computer-Aided Civil and Infrastructure Engineering, 34(8) 654-676 August 2019, which has been published in final form at https://onlinelibrary.wiley.com/doi/full/10.1111/mice.12451. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.oses in accordance with Wiley Terms and Conditions for Self-Archiving.
Type: article (author version)
URI: http://hdl.handle.net/2115/79011
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 前田 圭介

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