# Glass Defect Detection with Improved Data Augmentation under Total Reflection Lighting
## 1 Introduction  
The application of glass in numerous fields, such as mobile devices, household goods, industrial machinery, instruments and meters, and electronic products, has significantly boosted the demand for touch panel glass. The production of mobile phone flat glass requires deep processing operations including stretching, cutting, edging, and tempering [1]. During these operations, glass, as a transparent and smooth product, can be easily subjected to damage caused by mechanical or human operational errors. With defects such as pitting, scratches, chipping, and watermarks, the damaged glass fails to moveon to subsequent processing operations. In addition, transferring the production line of mobile phone flat glass following processing operations will un-avoidably result in dust in the air adhering to the surface of the mobile phone flat glass, thus degrading its quality. Currently, manual visual inspection work is a common method for detecting the defects in mobile phone flat glass. However, its relatively high subjectivity and uncertainty lead to low detection precision, a high false acceptance rate, and a high false rejection rate. With current conventional light sources, it is difficult to distinguish dust, a repairable defect, from other irreparable defects. For instance, the similar imaging characteristics of dust and point defects make it easy to make misjudgments, resulting in serious resource waste and quality problems. To solve the problems brought about by manual inspection, it has become urgent to realize the automatic defect inspection of mobile phone flat glass. The realization of cell phone flat glass defect detection strives to (1) achieve industrialized real-time detection and a fast detection speed; (2) achieve a good generalization of the detection method, which can be applied to the detection of a variety of defects; (3) achieve a high detection accuracy and try to avoid omissions and misdetections in the detection process; (4) realize multi-scale defect detection; and (5) effectively identify point defects and dust defects. With the rise of intelligent equipment and the rapid progress of machine vision technology, intelligent inspection equipment based on machine vision is characterized by a fast inspection speed, high accuracy, low cost, and strong adaptability. Since the end of the 1970s, scholars at home and abroad have conducted research on the defect detection of transparent media products, which roughly falls into two categories. One is identifying and detecting defects with traditional visual algorithms, and the other is with deep learning approaches. [PERSON] et al. [2] proposed an improved K-means clustering method based on backlight illumination to identify the defects in mobile phone flat glass with the test precision reaching 96.5%. Nevertheless, this method turns out to be less satisfying when it comes to low-contrast defect tests. With backlight illumination, [PERSON] et al. [3] acquired images, conducted denoising and segmentation operations on the images, and then built a BP neural network to identify and classify them, gaining a detection precision of 90.2%. However, there was a small sample size and a high false detection rate. Professor [PERSON]'s team from Huazhong University of Science and Technology [4; 5; 6; 7; 8; 9] put forward a method combining texture and a BP neural network to detect float glass defects. The algorithm, however, has high requirements for the quality of collected images and fails to have ideal applicability. [PERSON] [10; 11; 12] adopted the difference image method to identify the defects in mobile phone flat glass and reached a detection precision of 92%. Nevertheless, that method is greatly subject to natural illumination, resulting in low robustness in the algorithm. In recent years, target detection based on a deep convolution neural network has come into being [13; 14].