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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 <italic>et al.</italic>, 2017), Bilinear feature layers (Lin <italic>et al.</italic>, 2015), and squeeze-and-excitation blocks (Hu <italic>et al.</italic>, 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 <italic>et al.</italic>, 2012), VGG (Simonyan and Zisserman, 2014) and ResNet (He <italic>et al.</italic>, 2016), on both the public and our LGP datasets with less GPU memory consumption.

Effective Defect Detection Method Based on Bilinear Texture Features for LGPs