FedCrack: Federated Transfer Learning With Unsupervised Representation for Crack Detection-Reference-Cited by-同舟云学术

FedCrack: Federated Transfer Learning With Unsupervised Representation for Crack Detection

Published:2023-10 Issue:10 Volume:24 Page:11171-11184
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Jin Xiating¹^ORCID,Bu Jiajun²^ORCID,Yu Zhi¹^ORCID,Zhang Hui³^ORCID,Wang Yaonan³^ORCID

Affiliation:

1. Zhejiang Provincial Key Laboratory of Service Robot, College of Software Technology, Zhejiang University, Ningbo, China

2. Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China

3. National Engineering Laboratory of Robot Visual Perception and Control Technology, College of Electrical and Information Engineering, Hunan University, Changsha, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Zhejiang Provincial Key Research and Development Program of China

Ningbo Natural Science Foundation

Hunan Leading Talent of Technological Innovation

Hunan Science Fund for Distinguished Young Scholars

Hunan Provincial Key Research and Development Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10271405/10164678.pdf?arnumber=10164678

Reference70 articles.

1. DMA-Net: DeepLab With Multi-Scale Attention for Pavement Crack Segmentation

2. On data banks and privacy homomorphisms;rivest;Found Secure Comput,1978

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

4. Learning Disentangled Representation Implicitly Via Transformer for Occluded Person Re-Identification

5. CrackW-Net: A Novel Pavement Crack Image Segmentation Convolutional Neural Network

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