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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