Methodology for Selecting Neural Network Architecture for Recognizing Pavement Defects-Reference-Cited by-同舟云学术

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Methodology for Selecting Neural Network Architecture for Recognizing Pavement Defects

Published:2023-11-15 Issue: Volume: Page:
ISSN:
Container-title:2023 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED)
language:
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Author:

Tsesar A. A.¹,Nikitaev M. M.²,Barinov K. A.³,Vasiliev Yu. E.³,Mikolaichuk K. V.³

Affiliation:

1. Small Innovative Enterprise “Monitoring of Roads and Road Technologies” SIE “MADiDT”,Moscow,Russia

2. State Budgetary Institution of the City of Moscow, “Automobile Roads”,Moscow,Russia

3. Moscow Automobile and Road Construction State Technical University (MADI),Moscow,Russia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10332457/10332550/10332619.pdf?arnumber=10332619

Reference20 articles.

1. Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning

2. Application of Combining YOLO Models and 3D GPR Images in Road Detection and Maintenance

3. Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data

4. Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete

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