Improving the Classification Performance of Asphalt Cracks After Earthquake With a New Feature Selection Algorithm-Reference-Cited by-同舟云学术

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Improving the Classification Performance of Asphalt Cracks After Earthquake With a New Feature Selection Algorithm

Published:2024 Issue: Volume:12 Page:6604-6614
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Yılmaz Mehmet¹,Yalçın Erkut¹^ORCID,Kifah Saif²,Demir Fatih³^ORCID,Şengür Abdulkadir⁴^ORCID,Demir Rozerin¹,Mehmood Raja Majid²^ORCID

Affiliation:

1. Civil Engineering Department, Engineering Faculty, Fırat University, Elâzığ, Turkey

2. School of Computing and Data Science, Xiamen University Malaysia, Sepang, Malaysia

3. Software Engineering Department, Engineering Faculty, Fırat University, Elâzığ, Turkey

4. Electric and Electronic Department, Fýrat University, Elâzığ, Turkey

Funder

Xiamen University Malaysia Research Fund

Tubitak 1002C Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10388226.pdf?arnumber=10388226

Reference37 articles.

1. Practice for Roads and Parking Lots Pavement Condition Index Surveys

2. Deep convolution neural network-based transfer learning method for civil infrastructure crack detection

3. Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning

4. Asphalt Pavement Crack Detection Based on Convolutional Neural Network and Infrared Thermography

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