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

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.

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

10.1190/segj2018-031.1 ◽

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

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A deep convolutional neural network approach with information fusion for bearing fault diagnosis under different working conditions

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406220902181 ◽

2020 ◽

pp. 095440622090218

Author(s):

Tang Tang ◽

Tianhao Hu ◽

Ming Chen ◽

Ronglai Lin ◽

Guorui Chen

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Information Fusion ◽

Vibration Signal ◽

Deep Convolutional Neural Network ◽

Feature Representation ◽

Training Data ◽

Data Sets ◽

Neural Network Approach

In recent years, deep learning-based fault diagnosis methods have drawn lots of attention. However, for most cases, the success of machine learning-based models relies on the circumstance that training data and testing data are under the same working condition, which is too strict for real implementation cases. Combined with the features of robustness of deep convolutional neural network and vibration signal characteristics, information fusion technology is introduced in this study to enhance the feature representation capability as well as the transferability of diagnosis models. With the basis of multi-sensors and narrow-band decomposition techniques, a convolutional architecture named fusion unit is proposed to extract multi-scale features from different sensors. The proposed method is tested on two data sets and has achieved relatively higher generalization ability when compared with several existing works, which demonstrates the effectiveness of our proposed fusion unit for feature extraction on both source task and target task.

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Estimating ambient visibility in the presence of fog: a deep convolutional neural network approach

Personal and Ubiquitous Computing ◽

10.1007/s00779-019-01334-w ◽

2019 ◽

Cited By ~ 1

Author(s):

Fatma Outay ◽

Bilal Taha ◽

Hazar Chaabani ◽

Faouzi Kamoun ◽

Naoufel Werghi ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Deep Convolutional Neural Network ◽

Network Approach ◽

Neural Network Approach

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

10.1063/5.0053043 ◽

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

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Detection and Classification of Brain Tumors from MRI Images Using a Deep Convolutional Neural Network Approach

International Journal of Software Innovation ◽

10.4018/ijsi.293269 ◽

2022 ◽

Vol 10 (1) ◽

pp. 0-0

Keyword(s):

Neural Network ◽

Deep Learning ◽

Brain Tumor ◽

Convolutional Neural Network ◽

Binary Classification ◽

Deep Convolutional Neural Network ◽

Neural Network Models ◽

Neural Network Approach ◽

Cancer Disease

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the ﬁelds of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

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