Özkaya, UmutÖztürk, ŞabanMelgani, FaridSeyfi, Leventl2021-12-132021-12-1320210926-58051872-7891https://doi.org/10.1016/j.autcon.2020.103525https://hdl.handle.net/20.500.13091/1131In 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.eninfo:eu-repo/semantics/closedAccessGPRCNNBi-LSTMResidual connectionsArticle10.1016/j.autcon.2020.1035252-s2.0-85098545669