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.

Residual CNN + Bi-LSTM model to analyze GPR B scan images / Ozkaya, U.; Ozturk, Yagmur; Melgani, F.; Seyfi, L.. - In: AUTOMATION IN CONSTRUCTION. - ISSN 0926-5805. - 123:103525(2021), pp. 1035251-1035255. [10.1016/j.autcon.2020.103525]

Residual CNN + Bi-LSTM model to analyze GPR B scan images

Ozturk;Melgani F.;
2021-01-01

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.
2021
103525
Ozkaya, U.; Ozturk, Yagmur; Melgani, F.; Seyfi, L.
Residual CNN + Bi-LSTM model to analyze GPR B scan images / Ozkaya, U.; Ozturk, Yagmur; Melgani, F.; Seyfi, L.. - In: AUTOMATION IN CONSTRUCTION. - ISSN 0926-5805. - 123:103525(2021), pp. 1035251-1035255. [10.1016/j.autcon.2020.103525]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/329652
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