Manual surface inspection methods performed by quality inspectors do not satisfy the
continuously increasing quality standards of industrial manufacturing processes. Machine …

In the past years, the computer vision domain has been profoundly changed by the advent of
deep learning algorithms and data science. The defect detection problem is of outmost …

In this paper, the unsupervised autoencoder learning for automated defect detection in
manufacturing is evaluated, where only the defect-free samples are required for the model …

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

The use of image processing for the detection and classification of defects has been a reality
for some time in science and industry. New methods are continually being presented to …

Automated surface inspection (ASI) is critical to quality control in industrial manufacturing
processes. Recent advances in deep learning have produced new ASI methods that …

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 …

Detecting defects of yarn‐dyed fabrics automatically in industrial scenarios can improve
economic efficiency, but the scarcity of defect samples makes the task more challenging in …

Surface defect detection of strip steel is an important guarantee for improving the production
quality of strip steel. However, due to the diverse types, scales, and texture structures of …

In this study, an embedded machine vision system using Gabor filters and Pulse Coupled
Neural Network (PCNN) is developed to identify defects of warp-knitted fabrics …