Wood defect detection and classification using deep learning / Yap Yi Ren
In the timber and wood industry, natural defects on wood and timber are always one of the main issues. In many timber and wood industry, the quality assurance of the board is still controlled by a human. This is because the defects can vary in many ways likes amount, shape, area and colour. The qual...
Saved in:
Main Author: | Yap, Yi Ren |
---|---|
Format: | Thesis |
Published: |
2019
|
Subjects: | |
Online Access: | http://studentsrepo.um.edu.my/11441/1/Yap_Yi_Ren.jpg http://studentsrepo.um.edu.my/11441/8/yi_ren.pdf http://studentsrepo.um.edu.my/11441/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Evaluation of the Transfer Learning Models in Wafer Defects Classification
by: Jessnor Arif, Mat Jizat, et al.
Published: (2022) -
Project management case study - development of iron ore processing plant / Yap Park Wah
by: Yap , Park Wah
Published: (2022) -
Performance comparison between LQG and PID controllers for HDD's servo timing control system / Ng Yap Han
by: Ng , Yap Han
Published: (2018) -
Classification Analysis Of High Frequency Stress Wave For Autonomous Detection Of Defect In Steel Tubes
by: Abd Halim, Zakiah, et al.
Published: (2014) -
Defect severity classification of complex composites using CWT and CNN
by: Lim, Wilson, et al.
Published: (2022)