About

Chao-Ching Ho is a leading researcher in machine vision and intelligent robotic systems, with a career spanning over a decade of impactful contributions. His work centers on integrating advanced computer vision, deep learning, and servo control to solve complex industrial and real-world automation challenges. Ho’s major contributions include developing a deep residual neural network (DRNN) for defect detection on complex backgrounds, achieving 20 citations for its innovative modification of ResNet50 to enhance accuracy in automated inspection. He has also pioneered machine vision-based measurement systems, such as a delta robot with a motorized zoom lens for precise angle measurement between circular holes in bicycle derailleurs (13 citations), and an automated LED chip mounting system that overcomes position uncertainty (8 citations). Beyond industrial applications, Ho has explored dynamic environments with real-time fuzzy reasoning and stereo vision-based control for catching moving targets, like a goldfish (9 and 5 citations), and developed sensory fusion for intelligent fire-fighting robots (4 citations each). His notable achievements include designing a robotic sewing system and a solenoid housing placement system, showcasing his versatility in merging vision and robotics. With over 70 citations across his top papers, Ho’s work continues to inspire advancements in automated quality control and intelligent manufacturing.

Research Focus

Key Achievements

5
H-Index
11
Papers
74
Total Citations
7
Avg Citations/Paper
🏆 Most Cited Paper
Deep Residual Neural Network-Based Defect Detection on Complex Backgrounds
20 citations · 2022
📈 Most Prolific Year: 2009 (3 Papers)
🤝 Key Collaborators: 16
🏛 Institutions: National Taipei University of Technology, National Yunlin University of Science and Technology, National Taiwan University of Science and Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago