A Quality Control Application on a Smart Factory Prototype Using Deep Learning Methods

Abstract

The number of smart factories is increasing day after day to reach the vision of Industry 4.0. Computer vision and image processing have important roles in the systems whose aim is unmanned production. In the industrial automation applications, computer vision is mostly used at the quality control stage. In this stage, there are many applications which use image-processing methods for object detection and classification but deep learning-based applications are rarely seen. In this work, a visual quality control automation application is proposed by using a camera placed over the assembly line in a smart factor model. The product is detected in an image obtained from the assembly line and then classified as 'okay' or 'not okay' using deep learning methods. After the deep learning-based quality control, the 'okay' products continue their production stages and the 'not okay' products are separated from the production line using a PLC, which controls the line. It is seen with this application that deep learning methods in automation applications will have an important role in transitioning to the industry 4.0. © 2019 IEEE.