Minqi He
Papers
2
Total Citations
38
H-Index
2
About
Minqi He is a researcher advancing the fields of computer vision, robotic precision, and intelligent manufacturing. His work focuses on developing accurate detection and calibration methods for industrial automation, particularly in welding and machining processes. He is best known for proposing an improved SegNet network model for the accurate detection and segmentation of car body welding slags (2022, 22 citations), which enhances quality control in automotive manufacturing by enabling precise identification of defects. Additionally, He introduced the GWM-view method—a gradient-weighted multi-view calibration approach for machining robot positioning (2023, 16 citations)—which significantly improves the accuracy of robotic systems in complex industrial environments. His contributions are notable for bridging deep learning with practical manufacturing challenges, offering robust solutions that reduce errors and increase efficiency. With a growing citation impact, He’s work is increasingly recognized as foundational for next-generation automated production lines, making him a key figure in applied computer vision and robotics.
Research Focus
Key Achievements
Top Papers
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