Detection of Defective Wrap Roll Product Using Convolution Neural Networks

Abstract

Although the automation of inspection processes for various kinds of industrial products has progressed, the situation seems to be largely depending on visual inspection ability of inspectors who are familiar with the quality control of each product. Recently, not a few attempts have been tried to apply convolutional neural networks (CNNs) specialized in deep learning technology to image recognition for product defect detection. The authors have developed an application that can design and train CNNs and support vector machines (SVMs). In this paper, the application is tried to be applied to the defect detection in the manufacturing process of wrap roll product. Firstly, a template matching technique is used to extract only the target film areas from the entire images of wrap roll products. Next, a CNN named sssNet consisting of 15 layers is originally designed so as to classify input images into defective or not, then trained using a large number of original and augmented images to enhance the generalization ability. Finally, the trained sssNet is evaluated through classification experiments of test images. The usefulness of the developed application with a promising function of defect visualization is also assessed through this test trial.