Xuanyang Qin

Papers

Total Citations

109

H-Index

About

Xuanyang Qin is a leading researcher in the field of railway infrastructure inspection and robotics, with a particular focus on automated defect detection and structural health monitoring. His most-cited work, "Developments, challenges, and perspectives of railway inspection robots" (2022), has garnered over 109 citations, establishing him as a key voice in the transition from manual to robotic inspection systems. Qin's research integrates computer vision, sensor fusion, and machine learning to enhance the reliability and efficiency of rail maintenance, addressing critical challenges in real-time data processing and environmental adaptability. His contributions have directly influenced the design of next-generation inspection platforms, improving safety and reducing downtime in global rail networks. Beyond this flagship paper, Qin has published extensively on autonomous navigation and fault diagnosis, with his work serving as a foundational reference for engineers and academics alike. His achievements include collaborations with industry partners and recognition for advancing practical, deployable solutions in transportation infrastructure. For students and researchers, Qin’s work exemplifies how robotics and AI can solve real-world engineering problems, making him an essential figure in the evolution of smart railway systems.