Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1131
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Özkaya, Umut | - |
dc.contributor.author | Öztürk, Şaban | - |
dc.contributor.author | Melgani, Farid | - |
dc.contributor.author | Seyfi, Leventl | - |
dc.date.accessioned | 2021-12-13T10:34:43Z | - |
dc.date.available | 2021-12-13T10:34:43Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 0926-5805 | - |
dc.identifier.issn | 1872-7891 | - |
dc.identifier.uri | https://doi.org/10.1016/j.autcon.2020.103525 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/1131 | - |
dc.description.abstract | In this study, the residual Convolutional Neural Network (CNN) with the Bidirectional Long Short Time Memory (Bi-LSTM) model has proposed for the analysis of Ground Penetrating Radar B scan (GPR B Scan) images. GPR characteristics, scanning frequency, and soil type make it very difficult to analyze GPR B Scan images. Also, noise and clutter in the image make this problem more challenging. The proposed method shows high performance in determining the scanning frequency of GPR B Scan images, type of GPR device, and the type of soil. In particular, residual structures and types of Bi-LSTMs connection within the proposed method led to increasing the performance. The metric performance of the proposed method is higher compared to other transfer learning based CNN structures. | en_US |
dc.language.iso | en | en_US |
dc.publisher | ELSEVIER | en_US |
dc.relation.ispartof | AUTOMATION IN CONSTRUCTION | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | GPR | en_US |
dc.subject | CNN | en_US |
dc.subject | Bi-LSTM | en_US |
dc.subject | Residual connections | en_US |
dc.title | Residual CNN plus Bi-LSTM model to analyze GPR B scan images | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.autcon.2020.103525 | - |
dc.identifier.scopus | 2-s2.0-85098545669 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | Ozturk, Saban/0000-0003-2371-8173 | - |
dc.authorwosid | Ozturk, Saban/ABI-3936-2020 | - |
dc.identifier.volume | 123 | en_US |
dc.identifier.wos | WOS:000614760700002 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57191610477 | - |
dc.authorscopusid | 57191953654 | - |
dc.authorscopusid | 35613488300 | - |
dc.authorscopusid | 36242356400 | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | embargo_20300101 | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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1-s2.0-S0926580520311055-main.pdf Until 2030-01-01 | 588.41 kB | Adobe PDF | View/Open Request a copy |
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