Rethinking Lightweight Convolutional Neural Networks for Efficient and High-Quality Pavement Crack Detection-Reference-Cited by-同舟云学术

Rethinking Lightweight Convolutional Neural Networks for Efficient and High-Quality Pavement Crack Detection

Published:2024-01 Issue:1 Volume:25 Page:237-250
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Li Kai¹^ORCID,Yang Jie²,Ma Siwei¹^ORCID,Wang Bo³^ORCID,Wang Shanshe¹,Tian Yingjie⁴^ORCID,Qi Zhiquan⁴^ORCID

Affiliation:

1. National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing, China

2. School of Mathematics Sciences, University of Chinese Academy of Sciences, Beijing, China

3. School of Information Technology and Management, University of International Business and Economics, Beijing, China

4. Key Laboratory of Big Data Mining and Knowledge Management and the Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, China

Funder

National Natural Science Foundation of China

University of International Business and Economics (UIBE) Excellent Young Scholar Project

Scientific Research Laboratory of Artificial Intelligence (AI) Technology and Applications, University of International Business and Economics

Science and Technology Plan Project in Basic Public Welfare Class of Shaoxing City

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10403543/10402569.pdf?arnumber=10402569

Reference62 articles.

1. Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function

2. DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection

3. Automatic Road Pavement Assessment with Image Processing: Review and Comparison

4. Automatic Road Crack Detection Using Random Structured Forests

5. Automation of Pavement Surface Crack Detection using the Continuous Wavelet Transform