Weihua Gui
Papers
3
Total Citations
17
H-Index
2
About
Weihua Gui is a pioneering researcher in intelligent industrial automation, whose work bridges robotics, computer vision, and advanced manufacturing. His primary research areas include autonomous navigation for industrial mobile robots, deep learning-based defect detection, and vision systems for harsh industrial environments. Gui’s most impactful contribution is his 2024 paper on heuristic dense reward shaping for map-free navigation, which has already garnered 10 citations—a remarkable achievement for a recent publication. This work enables automatic mobile robots to navigate complex industrial settings without pre-built maps, significantly enhancing operational flexibility. In 2023, Gui developed a two-stage UNet network using mixed supervised learning for ingot oxide slag detection (5 citations), improving quality control in metal processing. His most innovative work, however, is a detection framework for semisolid metal slagging operations under strong light conditions (2 citations), which uniquely integrates Gabor feature extraction with multichannel PGAN to remove glare, identify working conditions, and evaluate slagging quality—a first-of-its-kind approach. Gui’s research directly addresses real-world industrial challenges, from foundry automation to robust vision systems, making him a rising leader in applied AI for manufacturing.
Research Focus
Key Achievements
Top Papers
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