Liu Wang

Shantou University, Wuhan Donghu University

Papers

Total Citations

H-Index

About

Liu Wang is a researcher working at the intersection of computer vision, deep learning, and intelligent infrastructure monitoring. His most notable contribution is in the domain of automated pavement crack detection, where he developed YOLOv5-AH, a customized deep learning model built upon the YOLOv5 architecture, published in 2022. This work addresses one of the persistent challenges in civil infrastructure assessment — accurately identifying pavement cracks despite complex backgrounds and variable crack scales — and has garnered 7 citations, reflecting growing interest from the road maintenance and computer vision communities. His research demonstrates a practical application of object detection techniques to real-world engineering problems, bridging the gap between advanced AI methodologies and infrastructure condition assessment. Wang has also explored robotics and multiprocessor control systems, investigating BS structure-based digital media frameworks for robot control, though this earlier 2020 work was subsequently retracted. Overall, Wang's research profile suggests a focus on applying machine learning and systems engineering to solve applied engineering challenges, with his pavement crack detection work standing as his most impactful and recognized contribution to date.