Papers
2
Total Citations
24
H-Index
2
About
Shuwen Zhang is a researcher specializing in computer vision, deep learning, and intelligent manufacturing, with a particular focus on industrial automation and robotic systems. Their most notable contribution is the development of an improved SegNet network model for the accurate detection and segmentation of car body welding slags, a critical task in automotive quality control. This work, published in 2022, has garnered 22 citations, underscoring its practical impact on manufacturing efficiency and defect detection. More recently, Zhang has advanced into motion control strategies for robotic arms, proposing a deep cascade feature Bayesian broad learning system that enhances precision and adaptability in automated environments. This innovative approach, introduced in 2025, has already attracted early citations, signaling its potential to reshape industrial robotics. Zhang’s research bridges the gap between theoretical deep learning architectures and real-world engineering challenges, offering scalable solutions for smart factories. Their work is particularly valuable for students and researchers interested in applied AI, computer vision for non-destructive testing, and the integration of learning systems into robotic control.
Research Focus
Key Achievements
Top Papers
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