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

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Institution:控制科学与工程学院

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

Journal:Automation in Construction

First Author:王静

Document Code:111C0DE77589428AA8CACF6B3FA87C95

Volume:133

Number of Words:7

Translation or Not:No

Date of Publication:2022-01

Release Time:2022-05-22

Pre One : GPRI2Net: A Deep-Neural-Network-Based Ground Penetrating Radar Data Inversion and Object Identification Framework for Consecutive and Long Survey Lines

Next One : Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds

Personal information

Professor

Gender : Male

Status : Employed

School/Department : 齐鲁交通学院

Date of Employment : 2018-09-17

Faculty/School : School of Qilu Transportation

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