Real-time defect detection in transparent multilayer polymer films using structured illumination and 1D filtering

2009 ◽  
Author(s):  
Walter Michaeli ◽  
Klaus Berdel ◽  
Oliver Osterbrink
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Polymer Films ◽  
Structured Illumination

scholarly journals Quality control in cloth production: a new system for real-time defect detection

2000 ◽  
Author(s):  
Antonio Baldassarre ◽  
Maurizio De Lucia ◽  
Francesca Rossi ◽  
Massimiliano Vannucci
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Defect Detection ◽  
Cloth Production ◽  
New System

Development of a real-time machine vision system for functional textile fabric defect detection using a deep YOLOv4 model

2021 ◽  
pp. 004051752110342
Author(s):  
Sifundvolesihle Dlamini ◽  
Chih-Yuan Kao ◽  
Shun-Lian Su ◽  
Chung-Feng Jeffrey Kuo
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Defect Detection ◽  
Data Augmentation ◽  
Detection System ◽  
Vision System ◽  
Good Precision ◽  
Machine Vision System ◽  
Time Machine ◽  
Textile Fabric

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 assist in textile industry quality control. The system consists of image acquisition hardware and image processing software. The software we developed uses data preprocessing techniques to break down raw images to smaller suitable sizes. Filtering is employed to denoise and enhance some features. To generalize and multiply the data to create robustness, we use data augmentation, which is followed by labeling where the defects in the images are labeled and tagged. Lastly, we utilize YOLOv4 for localization where the system is trained with weights of a pretrained model. Our software is deployed with the hardware that we designed to implement the detection system. The designed system shows strong performance in defect detection with precision of [Formula: see text], and recall and [Formula: see text] scores of [Formula: see text] and [Formula: see text], respectively. The detection speed is relatively fast at [Formula: see text] fps with a prediction speed of [Formula: see text] ms. Our system can automatically locate functional textile fabric defects with high confidence in real time.

In situ real time defect detection of 3D printed parts

2017 ◽  
Vol 17 ◽  
pp. 135-142 ◽  
Author(s):  
Oliver Holzmond ◽  
Xiaodong Li
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
3D Printed

Real-time joint transform correlation with photoanisotropic dye–polymer films

1994 ◽  
Vol 33 (32) ◽  
pp. 7634 ◽  
Author(s):  
Tizhi Huang ◽  
Kelvin H. Wagner
Keyword(s):  
Real Time ◽  
Polymer Films ◽  
Joint Transform Correlation

A real-time approach for automatic defect detection from PCBs based on SURF features and morphological operations

2019 ◽  
Vol 78 (24) ◽  
pp. 34437-34457 ◽  
Author(s):  
Abdel-Aziz I. M. Hassanin ◽  
Fathi E. Abd El-Samie ◽  
Ghada M. El Banby
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Morphological Operations ◽  
Surf Features

Detection of Surface and Subsurface Defects of Apples Using Structured- Illumination Reflectance Imaging with Machine Learning Algorithms

2018 ◽  
Vol 61 (6) ◽  
pp. 1831-1842 ◽  
Author(s):  
Yuzhen Lu ◽  
Renfu Lu
Keyword(s):  
Machine Learning ◽  
Empirical Mode Decomposition ◽  
Defect Detection ◽  
Support Vector ◽  
Structured Illumination ◽  
Uniform Illumination ◽  
Machine Learning Classification ◽  
Mode Decomposition ◽  
Better Than ◽  
Subsurface Defects

Abstract. Machine vision technology coupled with uniform illumination is now widely used for automatic sorting and grading of apples and other fruits, but it still does not have satisfactory performance for defect detection because of the large variety of defects, some of which are difficult to detect under uniform illumination. Structured-illumination reflectance imaging (SIRI) offers a new modality for imaging by using sinusoidally modulated structured illumination to obtain two sets of independent images: direct component (DC), which corresponds to conventional uniform illumination, and amplitude component (AC), which is unique for structured illumination. The objective of this study was to develop machine learning classification algorithms using DC and AC images and their combinations for enhanced detection of surface and subsurface defects of apples. A multispectral SIRI system with two phase-shifted sinusoidal illumination patterns was used to acquire images of ‘Delicious’ and ‘Golden Delicious’ apples with various types of surface and subsurface defects. DC and AC images were extracted through demodulation of the acquired images and were then enhanced using fast bi-dimensional empirical mode decomposition and subsequent image reconstruction. Defect detection algorithms were developed using random forest (RF), support vector machine (SVM), and convolutional neural network (CNN), for DC, AC, and ratio (AC divided by DC) images and their combinations. Results showed that AC images were superior to DC images for detecting subsurface defects, DC images were overall better than AC images for detecting surface defects, and ratio images were comparable to, or better than, DC and AC images for defect detection. The ensemble of DC, AC, and ratio images resulted in significantly better detection accuracies over using them individually. Among the three classifiers, CNN performed the best, with 98% detection accuracies for both varieties of apples, followed by SVM and RF. This research demonstrated that SIRI, coupled with a machine learning algorithm, can be a new, versatile, and effective modality for fruit defect detection. Keywords: Apple, Defect, Bi-dimensional empirical mode decomposition, Machine learning, Structured illumination.

Sign in / Sign up

Export Citation Format

Share Document