Automatic Defect Detection for Web Offset Printing Based on Machine Vision

• Main Authors: Erhu Zhang, Yajun Chen, Min Gao, Jinghong Duan, Cuining Jing
• Format: Article
• Language: English
• Published: MDPI AG 2019-09-01
• Series: Applied Sciences
• Subjects: web offset printing; defect detection; determination of first row; shape context; template creation
• Online Access: https://www.mdpi.com/2076-3417/9/17/3598

In the printing industry, defect detection is of crucial importance for ensuring the quality of printed matter. However, rarely has research been conducted for web offset printing. In this paper, we propose an automatic defect detection method for web offset printing, which consists of determining first row of captured images, image registration and defect detection. Determining the first row of captured images is a particular problem of web offset printing, which has not been studied before. To solve this problem, a fast computational algorithm based on image projection is given, which can convert 2D image searching into 1D feature matching. For image registration, a shape context descriptor is constructed by considering the shape concave-convex feature, which can effectively reduce the dimension of features compared with the traditional image registration method. To tolerate the position difference and brightness deviation between the detected image and the reference image, a modified image subtraction is proposed for defect detection. The experimental results demonstrate the effectiveness of the proposed method.