EVPTMFF: Bridge Crack Detection Based on Efficient Visual Pyramid Transformer and Multiple-Feature Fusion-Reference-Cited by-同舟云学术

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EVPTMFF: Bridge Crack Detection Based on Efficient Visual Pyramid Transformer and Multiple-Feature Fusion

Published:2024-08 Issue:4 Volume:38 Page:
ISSN:0887-3828
Container-title:Journal of Performance of Constructed Facilities
language:en
Short-container-title:J. Perform. Constr. Facil.

Author:

Li Gang¹,Zhou Pan²,Shen Dan³,Zhao Shanmeng⁴^ORCID

Affiliation:

1. Professor, School of Energy and Electric Engineering, Chang’an Univ., Xi’an 710064, China.

2. School of Electronics and Control Engineering, Chang’an Univ., Xi’an 710064, China (corresponding author).

3. Engineer, Dept. of Security, Chang’an Univ., Xi’an 710064, China.

4. Shaanxi Expressway Engineering Testing Inspection & Testing Co., Ltd., No. 57 Fengcheng Rd., Weiyang District, Xi’an 710086, China. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JPCFEV.CFENG-4709

Reference42 articles.

1. Analysis of Edge-Detection Techniques for Crack Identification in Bridges

2. Structural crack detection using deep convolutional neural networks

3. Curve‐based crack detection using crack information gain

4. Image Processing Algorithm for Real-Time Crack Inspection in Hole Expansion Test

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

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