open access

https://hdl.handle.net/2115/79011

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

英語

2019-08

John Wiley & Sons

Computer-aided civil and infrastructure engineering

34(8)

654-676

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.

学術雑誌論文

Accepted Manuscript

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.

1093-9687

1467-8667

AA11208825