Detection of target signatures in ground-penetrating radar images: A deep convolutional neural network approach

2019 ◽  
Author(s):  
Kazuya Ishitsuka ◽  
Shinichiro Iso ◽  
Kyosuke Onishi ◽  
Toshifumi Matsuoka
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ground Penetrating Radar ◽  
Deep Convolutional Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
Radar Images ◽  
Ground Penetrating

scholarly journals Object Detection in Ground-Penetrating Radar Images Using a Deep Convolutional Neural Network and Image Set Preparation by Migration

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Kazuya Ishitsuka ◽  
Shinichiro Iso ◽  
Kyosuke Onishi ◽  
Toshifumi Matsuoka
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ground Penetrating Radar ◽  
Deep Convolutional Neural Network ◽  
Training Data ◽  
Neural Network Approach ◽  
Radar Images ◽  
Image Set ◽  
Ground Penetrating ◽  
Better Than

Ground-penetrating radar allows the acquisition of many images for investigation of the pavement interior and shallow geological structures. Accordingly, an efficient methodology of detecting objects, such as pipes, reinforcing steel bars, and internal voids, in ground-penetrating radar images is an emerging technology. In this paper, we propose using a deep convolutional neural network to detect characteristic hyperbolic signatures from embedded objects. As a first step, we developed a migration-based method to collect many training data and created 53510 categorized images. We then examined the accuracy of the deep convolutional neural network in detecting the signatures. The accuracy of the classification was 0.945 (94.5%)–0.979 (97.9%) when using several thousands of training images and was much better than the accuracy of the conventional neural network approach. Our results demonstrate the effectiveness of the deep convolutional neural network in detecting characteristic events in ground-penetrating radar images.

Two-stream deep convolutional neural network approach for RGB-D face recognition

2021 ◽  
Author(s):  
P. Shunmugam ◽  
K. Kamarudin ◽  
L. M. Kamarudin ◽  
A. Zakaria ◽  
H. Nishizaki
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals A deep convolutional neural network approach for predicting phenotypes from genotypes

Planta ◽  
2018 ◽  
Vol 248 (5) ◽  
pp. 1307-1318 ◽  
Author(s):  
Wenlong Ma ◽  
Zhixu Qiu ◽  
Jie Song ◽  
Jiajia Li ◽  
Qian Cheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals A deep convolutional neural network approach for astrocyte detection

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Ilida Suleymanova ◽  
Tamas Balassa ◽  
Sushil Tripathi ◽  
Csaba Molnar ◽  
Mart Saarma ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Network Approach ◽  
Neural Network Approach
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