Development of Machine Vision System for Off-Line Inspection of Fine Defects on Glass Screen Surface

The complicated manufacturing process and poor industrial environment inevitably produce unacceptable scratches on glass screen surface. In this study, a machine vision system is developed to automatically detect fine scratch, which has a width smaller than 30 \mu \text{m} , on glass screen surface for the purpose of appearance quality assessment. The imaging hardware of the proposed machine vision system is made up of a high-resolution camera and a wide field lens so that a high-quality image is achieved in a relatively large image area. The captured glass screen images were first classified with YOLOv3 to exactly distinguish fine scratches in two other kinds of defects including dusts and stains. Then, the U-Net network was applied on the selected scratches to estimate their dimensions approximately. The proposed algorithms combination has a fast image processing speed and it is able to effectively reduce the false segmentation of other defects. The results of the developed machine vision system show a relatively good agreement with the well-established microscopic method. It demonstrates our machine vision method is able to image and quantitatively analyze scratches with dimensions between 25 and 100 \mu \text{m} .