Augmented Concrete Crack Segmentation: Learning Complete Representation to Defend Background Interference in Concrete Pavements-Reference-Cited by-同舟云学术

Augmented Concrete Crack Segmentation: Learning Complete Representation to Defend Background Interference in Concrete Pavements

Published:2024 Issue: Volume:73 Page:1-13
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Lang Hong¹^ORCID,Yuan Ye¹^ORCID,Chen Jiang¹^ORCID,Ding Shuo¹^ORCID,Lu Jian John¹^ORCID,Zhang Yong²^ORCID

Affiliation:

1. Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, China

2. Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Artificial Intelligence Institute, Faculty of Information Technology, Beijing University of Technology, Beijing, China

Funder

National Natural Science Foundation of China

Science and Technology Project of Henan Provincial Department of Transportation

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10474165.pdf?arnumber=10474165

Reference69 articles.

1. An Effective Hybrid Atrous Convolutional Network for Pixel-Level Crack Detection

2. Semiempirical Electromagnetic Modeling of Crack Detection and Sizing in Cement-Based Materials Using Near-Field Microwave Methods

3. SDDNet: A Fast and Accurate Network for Surface Defect Detection

4. Multiscale Attention Networks for Pavement Defect Detection

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