Real-Time Fabric Defect Segmentation Based on Convolutional Neural Network

Abstract

Automated visual inspection for quality control has widely-used deep convolutional neural networks (CNNs) in fabric defect detection. Most of the research on defect detection only focuses on increasing the accuracy of segmentation models with little attention to computationally efficient solutions. In this study, we propose a highly efficient deep learning-based method for pixel-level fabric defect classification algorithm based on a CNN. We started with the ShuffleNet V2 feature extractor, added five deconvolution layers as the decoder, and used a resize bilinear to produce the segmentation mask. To solve the sample imbalance problem, we used an improved loss function to guide network learning. We evaluated our model on the fabric defect data set. The proposed model outperformed the existing image segmentation models in both model efficiency and segmentation accuracy.