Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1131
Title: | Residual CNN plus Bi-LSTM model to analyze GPR B scan images | Authors: | Özkaya, Umut Öztürk, Şaban Melgani, Farid Seyfi, Leventl |
Keywords: | GPR CNN Bi-LSTM Residual connections |
Publisher: | ELSEVIER | 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. | URI: | https://doi.org/10.1016/j.autcon.2020.103525 https://hdl.handle.net/20.500.13091/1131 |
ISSN: | 0926-5805 1872-7891 |
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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