Effective Defect Detection Method Based on Bilinear Texture Features for LGPs

Libin Hong,Fuchang Liu,Dibin Zhou,Xianglei Wu

doi:10.1109/access.2021.3111410

Abstract

Automatic defect detection of light guide plates (LGPs) is an important task in the manufacture of liquid crystal displays. During thermo-printing, defects of tag lines on LGPs may occur easily, and these defects are of two categories: bubbles and missing tag lines. These defects lack salient visual attributes, such as edge-based and region-based features, and as such, traditional methods fail to detect them. To address this, we propose a Dense-bilinear convolutional neural network (BCNN), an end-to-end defect detection network, utilizing Dense-blocks (Huang et al. , 2017), Bilinear feature layers (Lin et al. , 2015), and squeeze-and-excitation blocks (Hu et al. , 2018). Our network exploits fine-grained texture features, which leads to parameter reduction and accuracy enhancement. We validate our network on our LGP dataset containing 5,860 images from three cases: bubbles, tag line existence, and tag line missing. Our network outperforms AlexNet (Krizhevsky et al. , 2012), VGG (Simonyan and Zisserman, 2014) and ResNet (He et al. , 2016), on both the public and our LGP datasets with less GPU memory consumption.

Highlights

I N recent years, liquid crystal displays have become increasingly thinner, and owing to this, the demand for high quality light guide plates (LGPs), which are core components of the backlight module, has increased
Defect detection of LGPs is an essential requirement in liquid crystal displays and can be performed using machine vision
This paper explored a deep-learning approach to surfacedefect detection with a texture classification network from the point of view of specific industrial application

Summary

Introduction

I N recent years, liquid crystal displays have become increasingly thinner, and owing to this, the demand for high quality light guide plates (LGPs), which are core components of the backlight module, has increased. Defect detection of LGPs is an essential requirement in liquid crystal displays and can be performed using machine vision. Traditional detection methods usually involve image preprocessing for the extraction of edges or regions of LGPs for locating the tag line. LGPs have good light transmittance, which causes the image edges to blur and the regions to become inconsistent. This renders the traditional image preprocessing and defect detection algorithms ineffective. Deep learning-based methods provide flexible solutions that can be quickly adapted to new types of products, only using the appropriate number of training images [12]

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