Weibo Ning

Papers

Total Citations

H-Index

About

Weibo Ning is a researcher at the forefront of applying deep learning and robotics to solve real-world engineering and medical challenges. His primary research areas span computer vision, object detection, and intelligent robotic systems, with a particular focus on automated infrastructure inspection and precision surgical assistance. Ning’s most notable contribution is the development of YOLOv5-AH, a novel deep learning model designed for automatic pavement crack detection. This work directly addresses the critical problem of assessing road conditions by overcoming challenges posed by complex backgrounds and varying crack scales, achieving 7 citations and providing a practical tool for infrastructure maintenance. In a pioneering move into medical robotics, Ning also introduced a novel robot system for hair transplant surgery, integrating self-calibration and structured light hair follicle detection. This system aims to advance beyond traditional extraction and transplantation methods, offering a path toward automated follicular unit multiplication. With a growing citation footprint, Weibo Ning is establishing himself as an innovator who bridges the gap between advanced AI perception and tangible, life-improving technologies.