Evaluation of Fabric Defect Detection Based on Transfer Learning with Pre-trained AlexNet

Abstract

Deep learning methods are successful in many different domains such as image, natural language and signal processing. However, the number of samples affects success of deep learning algorithms significantly. Therefore, it is seen as a big challenge to obtain or produce lots of labeled data. A transfer learning method has been proposed to overcome this problem. Transfer learning aimed that using a pre-trained network instead of training it from scratch as the basis for new problem. In this paper, it is looked for a solution to fabric defect detection problem through transfer learning. The sale of defective fabrics damages both producers and customers. Accurate and rapid detection of fabric defects is a crucial problem for the textile industry. Since fabric has the features of unique own textures, it is a matter of curiosity how the transfer learning method will result in determining the fabric defect. In this study, using the AlexNet model trained with millions of images, the success rate of training from stratch to 75% was increased to 98% with transfer learning.