In modern manufacturing, vision-based defect recognition is an essential technology to
guarantee product quality, and it plays an important role in industrial intelligence. With the …

Deep learning–based fabric defect detection methods have been widely investigated to
improve production efficiency and product quality. Although deep learning–based methods …

Detecting and locating surface defects in textured materials is a crucial but challenging
problem due to factors such as texture variations and lack of adequate defective samples …

In this paper, we propose a discriminative representation for patterned fabric defect
detection when only limited negative samples are available. Fabric patches are efficiently …

In order to overcome the shortcoming that a fabric defect detection method can only fit into a
certain type of fabric, this paper presents a novel method by integrating the idea of the …

Purpose The purpose of this paper is to focus on the design and development of computer-
aided fabric defect detection and classification employing advanced learning algorithm …

We introduce a real-time machine vision system we developed with the aim of detecting
defects in functional textile fabrics with good precision at relatively fast detection speeds to …

To reduce labor costs for manual extract image features of yarn-dyed fabric defects, a
method based on YOLOV2 is proposed for yarn-dyed fabric defect automatic localization …

The productivity of textile industry is positively correlated with the efficiency of fabric defect
detection. Traditional manual detection methods have gradually been replaced by deep …

In this paper, inspired by an inherent characteristic of human visual system capable of
recognizing salient regions from a complicated scene, we treat a defective region as a …