Zuoquan Zhao

Chinese University of Hong Kong, Zhejiang University

Papers

3

Total Citations

103

H-Index

3

About

Zuoquan Zhao is a researcher at the forefront of intelligent infrastructure inspection and autonomous mobile robotics. His most impactful work centers on advancing visual inspection technologies for civil infrastructure, where he has made significant contributions to crack classification, segmentation, and detection. His comprehensive 2022 review and algorithm comparison, which has garnered 88 citations, serves as a critical benchmark for researchers and practitioners seeking to boost the reliability of automated infrastructure assessment. Beyond structural health monitoring, Zhao has pioneered robust solutions for mobile robot autonomy. His work on fault-tolerant architectures addresses a fundamental challenge in multi-sensor fusion for robot localization, proposing a system designed to maintain operational integrity even when individual sensors fail—a critical advancement for real-world deployment. Additionally, his development of a people-following system based on Laser Range Finders (LRF) demonstrates a commitment to practical, low-complexity solutions for human-robot interaction. By tackling both the high-stakes domain of infrastructure safety and the foundational challenges of reliable robot perception, Zhao’s research bridges the gap between theoretical robustness and applied engineering, making his work essential reading for those building the next generation of autonomous systems.

Research Focus

Key Achievements

3
H-Index
3
Papers
103
Total Citations
34
Avg Citations/Paper
🏆 Most Cited Paper
Datasets and processing methods for boosting visual inspection of civil infrastructure: A comprehensive review and algorithm comparison for crack classification, segmentation, and detection
88 citations · 2022
📈 Most Prolific Year: 2022 (1 Papers)
🤝 Key Collaborators: 11
🏛 Institutions: Chinese University of Hong Kong, Zhejiang University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago