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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Convolutional sparse coding-based deep random vector functional link network for distress classification of road structures
| 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)
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Submitter: 前田 圭介
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