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

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

scholarly journals Real-time fabric defect detection based on multi-scale convolutional neural network

2020 ◽  
Vol 2 (4) ◽  
pp. 189-196
Author(s):  
Shuxuan Zhao ◽  
Li Yin ◽  
Jie Zhang ◽  
Junliang Wang ◽  
Ray Zhong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Defect Detection ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Fabric Defect

scholarly journals Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 89278-89291 ◽  
Author(s):  
Jing Yang ◽  
Shaobo Li ◽  
Zheng Wang ◽  
Guanci Yang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Defect Detection ◽  
Detection System ◽  
Tiny Part
Sign in / Sign up

Export Citation Format

Share Document