Detection of
wood sheet surface defects using image processing is a
complicated problem in the forest industry as the image
of the wood surface contains kinds of
defects in different sizes,
colors and textures. The automatic optical inspection
(AOI) technology was introduced into the wood
manufacturing line to improve
production efficiency and
maintain the quality of products for years. However,
deep learning has achieved great success and
outperformed traditional computer
vision methods at present.
With the development of fully convolutional networks
(FCN), convolutional neural networks (CNN) can perform
pixel-wise classification
for image semantic
segmentation.
A well-performing segmentation
architecture in open datasets, DeepLab is practiced in
this thesis compared with the baseline model U-Net. The
image preprocessing by cutting off the redundant
background area can improve the training result. Also,
by applying the top k percent mining method to DeepLab,
the mean
intersection over union (MIoU) can be
boosted a lot, and the recall rate is up to 70% with the
improvement. The results show DeepLab is good at
capturing contextual information and suitable for wood
sheet surface defects detection. Furthermore, the defect
detection process established in this thesis can be
applied on other industrial detection besides wood
sheet.
Keyword：Artificial intelligence, Deep
learning, Semantic segmentation, Defect detection, Wood
sheet
