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
Key Words:Arbitrarily-oriented defect detection;Automation;Deep learning;Ground penetrating radar;Tunnel inspection
First Author:王静
Document Code:1523583211215523842
Volume:133
Number of Words:50
Translation or Not:No
Date of Publication:2021-11
Release Time:2024-10-01
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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