Some good practices for applying convolutional neural networks to buried threat detection in Ground Penetrating Radar

2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR) ◽

10.1109/iwagpr.2017.7996100 ◽

2017 ◽

Author(s):

Daniel Reichman ◽

Leslie M. Collins ◽

Jordan M. Malof

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Ground Penetrating Radar ◽

Threat Detection ◽

Good Practices ◽

Ground Penetrating

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Convolutional Neural Networks for Water Content Classification and Prediction With Ground Penetrating Radar

IEEE Access ◽

10.1109/access.2019.2960768 ◽

2019 ◽

Vol 7 ◽

pp. 185385-185392 ◽

Author(s):

Jing Zheng ◽

Xingzhi Teng ◽

Jie Liu ◽

Xu Qiao

Keyword(s):

Neural Networks ◽

Water Content ◽

Convolutional Neural Networks ◽

Ground Penetrating Radar ◽

Content Classification ◽

Ground Penetrating

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Application of Convolutional and Recurrent Neural Networks for Buried Threat Detection Using Ground Penetrating Radar Data

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.2978763 ◽

2020 ◽

Vol 58 (10) ◽

pp. 7022-7034

Author(s):

Mahdi Moalla ◽

Hichem Frigui ◽

Andrew Karem ◽

Abdelhamid Bouzid

Keyword(s):

Neural Networks ◽

Ground Penetrating Radar ◽

Recurrent Neural Networks ◽

Threat Detection ◽

Ground Penetrating

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Transfer and multitask learning using convolutional neural networks for buried wire detection from ground penetrating radar data

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV ◽

10.1117/12.2518875 ◽

2019 ◽

Author(s):

Enver Aydin ◽

Seniha Esen Yüksel Erdem

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Ground Penetrating Radar ◽

Multitask Learning ◽

Ground Penetrating

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Improving convolutional neural networks for buried target detection in ground penetrating radar using transfer learning via pretraining

10.1117/12.2263112 ◽

2017 ◽

Author(s):

John Bralich ◽

Daniël Reichman ◽

Leslie M. Collins ◽

Jordan M. Malof

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Target Detection ◽

Convolutional Neural Networks ◽

Ground Penetrating Radar ◽

Ground Penetrating

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Recognizing subsurface target responses in ground penetrating radar data using convolutional neural networks

10.1117/12.2177747 ◽

2015 ◽

Author(s):

Rayn T. Sakaguchi ◽

Kenneth D. Morton ◽

Leslie M. Collins ◽

Peter A. Torrione

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Ground Penetrating Radar ◽

Ground Penetrating

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Hyperbola Recognition from Ground Penetrating Radar Using Deep Convolutional Neural Networks

DEStech Transactions on Computer Science and Engineering ◽

10.12783/dtcse/aita2017/15982 ◽

2017 ◽

Author(s):

Zhi-jun LONG ◽

Bang-an XING ◽

Hai LIU ◽

Qing-huo LIU

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Ground Penetrating Radar ◽

Deep Convolutional Neural Networks ◽

Ground Penetrating

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Arbitrarily-oriented tunnel lining defects detection from Ground Penetrating Radar images using deep Convolutional Neural networks

Automation in Construction ◽

10.1016/j.autcon.2021.104044 ◽

2022 ◽

Vol 133 ◽

pp. 104044

Author(s):

Jing Wang ◽

Jiaqi Zhang ◽

Anthony G. Cohn ◽

Zhengfang Wang ◽

Hanchi Liu ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Ground Penetrating Radar ◽

Tunnel Lining ◽

Deep Convolutional Neural Networks ◽

Radar Images ◽

Ground Penetrating

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A robust Bayesian approach to target detection applied to explosive threat detection in handheld ground penetrating radar data

10.1117/12.2050514 ◽

2014 ◽

Author(s):

Kenneth D. Morton ◽

Leslie M. Collins ◽

Peter A. Torrione

Keyword(s):

Target Detection ◽

Bayesian Approach ◽

Ground Penetrating Radar ◽

Threat Detection ◽

Ground Penetrating

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Histogram of gradient features for buried threat detection in ground penetrating radar data

2012 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2012.6350748 ◽

2012 ◽

Author(s):

Peter Torrione ◽

Kenneth D. Morton ◽

Rayn Sakaguchi ◽

Leslie M. Collins

Keyword(s):

Ground Penetrating Radar ◽

Threat Detection ◽

Ground Penetrating

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If training data appears to be mislabeled, should we relabel it? Improving supervised learning algorithms for threat detection in ground penetrating radar data

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII ◽

10.1117/12.2305881 ◽

2018 ◽

Author(s):

Daniël Reichman ◽

Leslie M. Collins ◽

Jordan M. Malof

Keyword(s):

Supervised Learning ◽

Ground Penetrating Radar ◽

Learning Algorithms ◽

Training Data ◽

Threat Detection ◽

Supervised Learning Algorithms ◽

Ground Penetrating

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