Depth-Assisted Semi-Supervised RGB-D Rail Surface Defect Inspection-Reference-Cited by-同舟云学术

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Depth-Assisted Semi-Supervised RGB-D Rail Surface Defect Inspection

Published:2024-07 Issue:7 Volume:25 Page:8042-8052
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Wang Jie¹^ORCID,Li Guoqiang²,Qiu Guanwen³,Ma Gang⁴,Xi Jinwen⁵^ORCID,Yu Nana¹^ORCID

Affiliation:

1. College of Intelligence and Computing, Tianjin University, Tianjin, China

2. National Science Library, Chinese Academy of Sciences, Beijing, China

3. School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

4. BOE Technology Group Company Ltd., Beijing, China

5. Zhongguancun Laboratory, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10582787/10508233.pdf?arnumber=10508233

Reference70 articles.

1. Unsupervised Saliency Detection of Rail Surface Defects using Stereoscopic Images

2. Modal Evaluation Network via Knowledge Distillation for No-Service Rail Surface Defect Detection

3. Surface Defect Detection for No-Service Rails With Skeleton-Aware Accurate and Fast Network

4. An image-level weakly supervised segmentation method for No-service rail surface defect with size prior

5. Automated Visual Defect Detection for Flat Steel Surface: A Survey

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Defect detection on multi-type rail surfaces via IoU decoupling and multi-information alignment;Advanced Engineering Informatics;2024-10

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