Yen‐Chang Huang
Papers
3
Total Citations
48
H-Index
3
About
Yen‐Chang Huang is a researcher whose work bridges computer vision, manufacturing quality control, and industrial automation. His most impactful contribution is in automatic defect segmentation on leather using deep learning—a 2019 paper with 37 citations that addresses a critical challenge in the leather industry, where surface defects directly affect material value. By applying convolutional neural networks to this traditionally manual inspection task, Huang provided a scalable, objective solution for quality assessment. He has also explored volume prediction for ellipsoidal ham using statistical methods (7 citations), demonstrating versatility in applying computational techniques to agricultural and food processing contexts. More recently, his 2024 work on AGV indoor localization—using drawstring displacement sensors for high-fidelity positioning and map building (4 citations)—shows a shift toward robotics and autonomous navigation in industrial environments. Huang’s research consistently targets practical, real-world problems, from material inspection to logistics, making his work relevant for engineers and researchers in smart manufacturing and computer vision.
Research Focus
Key Achievements
Top Papers
- 1Automatic Defect Segmentation on Leather with Deep Learning37 citations · 2019
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