Visual perception refers to the process of organizing, identifying, and interpreting visual
information in environmental awareness and understanding. With the rapid progress of …

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

To improve efficiency and classification accuracy, and overcome the issue of poor
generalization performance of traditional fabric defect classification methods, we present a …

In medical world, wound care and follow-up is one of the issues that are gaining importance
to work on day by day. Accurate and early recognition of wounds can reduce treatment …

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 …

Fabric defect detection plays an important role in the textile production process, but there are
still some challenges in detecting defects rapidly and accurately. In this paper, we propose a …

Fabric defect classification is traditionally achieved by human visual examination, which is
inefficient and labor-intensive. Therefore, using intelligent and automated methods to solve …

Defect inspection plays an essential role in ensuring quality of industrial products. The most
widely used human visual inspection method has some drawbacks such as high cost and …

The convolutional neural network (CNN) has recently achieved great breakthroughs in many
computer vision tasks. However, its application in fabric texture defects classification has not …

The detection and location of yarn-dyed fabric defects is a crucial and challenging problem
in actual production scenarios. Recently, unsupervised fabric defect detection methods …