Optimizing Concrete Crack Detection: An Attention-Based SWIN U-Net Approach-Reference-Cited by-同舟云学术

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Optimizing Concrete Crack Detection: An Attention-Based SWIN U-Net Approach

Published:2024 Issue: Volume:12 Page:77575-77585
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Sarhadi Ali¹^ORCID,Ravanshadnia Mehdi¹^ORCID,Monirabbasi Armin²,Ghanbari Milad³^ORCID

Affiliation:

1. Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2. Department of Civil Engineering, Payame Noor University, Tehran, Iran

3. Department of Civil Engineering, East Tehran Branch, Islamic Azad University, Tehran, Iran

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10535162.pdf?arnumber=10535162

Reference34 articles.

1. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

2. Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network

3. Deep Learning Based Automatic Crack Detection and Segmentation for Unmanned Aerial Vehicle Inspections

4. Image-based concrete crack detection in tunnels using deep fully convolutional networks

5. Crack semantic segmentation using the U-Net with full attention strategy;Lin;arXiv:2104.14586,2021

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