DA-RDD: Toward Domain Adaptive Road Damage Detection Across Different Countries

Author:

Lin Chunmian1ORCID,Tian Daxin1ORCID,Duan Xuting1ORCID,Zhou Jianshan1ORCID,Zhao Dezong2ORCID,Cao Dongpu3ORCID

Affiliation:

1. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beijing Advanced Innovation Center for Big Data and Brain Computing, School of Transportation Science and Engineering, Beihang University, Beijing, China

2. James Watt School of Engineering, University of Glasgow, Glasgow, U.K.

3. Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Canada

Funder

National Natural Science Foundation of China

Natural Science Foundation of Beijing Municipality

Zhuoyue Program of Beihang University

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Cited by 8 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. LMFE-RDD: a road damage detector with a lightweight multi-feature extraction network;Multimedia Systems;2024-06-14

2. From global challenges to local solutions: A review of cross-country collaborations and winning strategies in road damage detection;Advanced Engineering Informatics;2024-04

3. Vehicle Damage Identification using Deep Learning Techniques;2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS);2024-02-24

4. YOLOX-RDD: A Method of Anchor-Free Road Damage Detection for Front-View Images;IEEE Transactions on Intelligent Transportation Systems;2024

5. Promoting Automatic Detection of Road Damage: A High-Resolution Dataset, a New Approach, and a New Evaluation Criterion;IEEE Transactions on Automation Science and Engineering;2024

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