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.