Ya-Hsin Teng
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Ya-Hsin Teng has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Chien-Hao Tseng
National Center for High-Performance Computing, National Applied Research Laboratories, Hsinchu 30076, Taiwan
1 shared publication - Lun-Chi Chen
College of Engineering, Tunghai University, Taichung, Taiwan
1 shared publication - Ruey-Kai Sheu
Department of Computer Science, Tunghai University, Taichung 407224, Taiwan
1 shared publication
Latest Publications
- Open Access
- 11 February 2020
- Published by MDPI AG in Applied Sciences
- Vol. 10 (4) , 1206
Apparatus and Method of Defect Detection for Resin Films
A defect inspection of resin films involves processes of detecting defects, size measuring, type classification and reflective action planning. It is not only a process requiring heavy investment in workforce, but also a tension between quality assurance with a 50-micrometer tolerance and visibility of the naked eye. To solve the difficulties of the workforce and time consumption processes of defect inspection, an apparatus is designed to collect high-quality images in one shot by leveraging a large field-of-view microscope at 2K resolution. Based on the image dataset, a two-step method is used to first locate possible defects and predict their types by a defect-shape-based deep learning model using the LeNet-5-adjusted network. The experimental results show that the proposed method can precisely locate the position and accurately inspect the fine-grained defects of resin films.
- 11 February 2020
- Published by MDPI AG in Applied Sciences
- Vol. 10 (4) , 1206
Open AccessApparatus and Method of Defect Detection for Resin Films
A defect inspection of resin films involves processes of detecting defects, size measuring, type classification and reflective action planning. It is not only a process requiring heavy investment in workforce, but also a tension between quality assurance with a 50-micrometer tolerance and visibility of the naked eye. To solve the difficulties of the workforce and time consumption processes of defect inspection, an apparatus is designed to collect high-quality images in one shot by leveraging a large field-of-view microscope at 2K resolution. Based on the image dataset, a two-step method is used to first locate possible defects and predict their types by a defect-shape-based deep learning model using the LeNet-5-adjusted network. The experimental results show that the proposed method can precisely locate the position and accurately inspect the fine-grained defects of resin films.