Qianyi Wan
Papers
1
Total Citations
1
H-Index
1
About
Qianyi Wan is a researcher focused on the intersection of underwater robotics and machine vision, with a particular emphasis on autonomous inspection systems. Their most notable work, "Implementation of Underwater Vehicle Pipeline Inspection Based on Machine Vision" (2022), introduces a practical framework for using computer vision algorithms to guide unmanned underwater vehicles in detecting and analyzing pipeline infrastructure. This contribution addresses critical challenges in offshore energy and marine engineering, where manual inspection is costly and hazardous. While the paper has garnered 1 citation to date, its relevance to the growing field of subsea automation signals potential for broader impact as underwater vehicle technologies advance. Wan’s research bridges the gap between theoretical vision models and real-world deployment, offering a foundation for future work in autonomous marine robotics. Their work is particularly valuable for students and engineers exploring how machine learning and sensor integration can enhance the reliability of underwater inspection tasks, a key area for environmental monitoring and industrial maintenance.
Research Focus
Key Achievements
Top Papers
- 1