Yonghua Shi
Papers
7
Total Citations
96
H-Index
4
About
Yonghua Shi is a researcher specializing in robotic welding systems, machine vision sensing, and intelligent automation for welding processes. Over nearly two decades, Shi has made sustained contributions to advancing the precision and autonomy of welding robots, with a particular focus on seam tracking, weld deviation detection, and real-time penetration prediction. Among Shi's most influential work is a 2016 study on weld deviation detection using wide dynamic range vision sensors in MAG welding, which has garnered 42 citations and stands as a benchmark contribution to vision-based welding monitoring. Early research from 2007 demonstrated a laser vision-equipped robotic welding system designed for challenging underwater engineering environments, reflecting Shi's commitment to extending automation into demanding real-world conditions. More recently, a 2023 paper introduced a segmentation-LSTM model deployed on embedded systems for real-time K-TIG welding penetration prediction, earning 22 citations and highlighting Shi's embrace of deep learning methodologies. A 2024 study further advanced passive stereo vision for three-dimensional seam tracking using a single HDR camera, showcasing continued innovation. Collectively, Shi's body of work bridges classical image processing techniques with modern artificial intelligence, offering practical, deployable solutions that have meaningfully shaped intelligent robotic welding research.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7Subpixel Accuracy for Extracting Groove Center Based on Corner Detection2 citations · 2006