Inspection systems have been widely applied in industries to examine defects in products. Investigations into the method of defect inspection are important for manufacturing process. In this paper, we present an inspection approach based on deep learning, with a view to distinguishing flawed smartphone cover glass and to classify different kinds of glass defects. In contrast with traditional techniques, image processing techniques (IPTs), which often creates challenges to algorithm calculations due to inconsistent real-world variables like lighting and shadow changes, deep learning-based defect inspection technique is less time-consuming. This study proposes a method using deep learning architecture of convolutional neuron networks (CNNs) for detecting cover glass defects. We cropped the images into pieces sized 300×300 pixel resolutions and classified defects features like scratches, dust, dirt and fur, which were subsequently learned automatically by different models of CNN, including LeNet-5, AlexNet, VGG and GoogLeNet. As the CNN finished auto-learning, it was then capable of detecting and classifying different kinds of defects. The best results among the used models showed that the accuracy of a trained network was 98% when classifying cover glass defects in the right categories, proven to perform better compared with IPT. This paper concludes that the proposed method can indeed find the defects in realistic situations with a more ease manner.

Keywords: Artificial Intelligence, Deep Learning, Convolutional Neural Network,Classification, Automated Optical Inspection, Glass