Surface Defect Detection for Mobile Phone Back Glass Based on Symmetric Convolutional Neural Network Deep Learning

Abstract

Defect detection based on machine vision and machine learning techniques has drawn much attention in recent years. Deep learning is very suitable for such segmentation and detection tasks and has become a promising research area. Surface quality inspection is essentially important in the manufacturing of mobile phone back glass (MPBG). Different types of defects are produced because of the imperfection of the manufacturing technique. Unlike general transparent glass, screen printing glass has totally different reflection and scattering characteristics, which means the traditional dark-field imaging system is not suitable for this task. Meanwhile, the imaging system requires high resolution since the minimum defect size can be 0.005 mm2. According to the imaging characteristics of screen printing glass, this paper proposes a coaxial bright-field (CBF) imaging system and low-angle bright-field (LABF) imaging system, and 8K line-scan complementary metal oxide semiconductor(CMOS) cameras are utilized to capture images with the resolution size of 16,000*8092. The CBF system is applied for the weak-scratch and discoloration defects while the LABF system is applied for the dent defect. A symmetric convolutional neural network composed of encoder and decoder structures is proposed based on U-net, which produces a semantic segmentation with the same size as the original input image. More than 10,000 original images were captured, and more than 30,000 defective and non-defective images were manually annotated in the glass surface defect dataset (GSDD). Verified by the experiments, the results showed that the average precision reaches more than 91% and the average recall rate reaches more than 95%. The method is very suitable for the surface defect inspection of screen printing mobile phone back glass.

Keywords

• defect detection
• deep learning
• screen printing glass
• machine vision