Real time fabric defect detection system on Matlab and C++/Opencv platforms

2017 ◽  
Author(s):  
Kazim Hanbay ◽  
Sedat Golgiyaz ◽  
Muhammed Fatih Talu
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Detection System ◽  
Fabric Defect Detection ◽  
Fabric Defect

scholarly journals Real time fabric defect detection system on an embedded DSP platform

Optik ◽  
2013 ◽  
Vol 124 (21) ◽  
pp. 5280-5284 ◽  
Author(s):  
Jagdish Lal Raheja ◽  
Bandla Ajay ◽  
Ankit Chaudhary
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Detection System ◽  
Fabric Defect Detection ◽  
Embedded Dsp ◽  
Fabric Defect

scholarly journals Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data

2020 ◽  
Vol 10 (7) ◽  
pp. 2511
Author(s):  
Young-Joo Han ◽  
Ha-Jin Yu
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Machine Vision ◽  
Image Classification ◽  
Defect Detection ◽  
Detection System ◽  
Image Detection ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Segmentation Image

As defect detection using machine vision is diversifying and expanding, approaches using deep learning are increasing. Recently, there have been much research for detecting and classifying defects using image segmentation, image detection, and image classification. These methods are effective but require a large number of actual defect data. However, it is very difficult to get a large amount of actual defect data in industrial areas. To overcome this problem, we propose a method for defect detection using stacked convolutional autoencoders. The autoencoders we proposed are trained by using only non-defect data and synthetic defect data generated by using the characteristics of defect based on the knowledge of the experts. A key advantage of our approach is that actual defect data is not required, and we verified that the performance is comparable to the systems trained using real defect data.

Fabric Defect Detection System

2021 ◽  
pp. 788-800
Author(s):  
Tanjim Mahmud ◽  
Juel Sikder ◽  
Rana Jyoti Chakma ◽  
Jannat Fardoush
Keyword(s):  
Defect Detection ◽  
Detection System ◽  
Fabric Defect Detection ◽  
Fabric Defect

scholarly journals Texture defect detection by using polynomial interpolation and multilayer perceptron

2019 ◽  
Vol 14 ◽  
pp. 155892501882527
Author(s):  
Sabeur Abid
Keyword(s):  
Defect Detection ◽  
Manufacturing Industry ◽  
Polynomial Interpolation ◽  
Detection System ◽  
Labor Cost ◽  
Fabric Defect Detection ◽  
Accuracy Comparison ◽  
Comparison Results ◽  
Fabric Defect ◽  
Texture Defect Detection

This article deals with fabric defect detection. The quality control in textile manufacturing industry becomes an important task, and the investment in this field is more than economical when reduction in labor cost and associated benefits are considered. This work is developed in collaboration with “PARTNER TEXTILE” company which expressed its need to install automated defect fabric detection system around its circular knitting machines. In this article, we present a new fabric defect detection method based on a polynomial interpolation of the fabric texture. The different image areas with and without defects are approximated by appropriate interpolating polynomials. Then, the coefficients of these polynomials are used to train a neural network to detect and locate regions of defects. The efficiency of the method is shown through simulations on different kinds of fabric defects provided by the company and the evaluation of the classification accuracy. Comparison results show that the proposed method outperforms several existing ones in terms of rapidity, localization, and precision.

Fast and Parallel Summed Area Table for Fabric Defect Detection

2016 ◽  
Vol 30 (09) ◽  
pp. 1660004 ◽  
Author(s):  
Khaled Ragab
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Gpu Computing ◽  
Processing Unit ◽  
Detection Algorithms ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Time Requirements ◽  
Robustness To Noise ◽  
Graphical Processing

Automating fabric defect detection has a significant role in fabric industries. However, the existing fabric defect detection algorithms lack the real-time performance that is required in real applications due to their high demanding computation. To ensure real time, high accuracy and reliable fabric defect detection this paper developed a fast and parallel normalized cross-correlation algorithm based on summed-area table technique called PFDD-SAT. To meet real-time requirements, extensive use of the NVIDIA CUDA framework for Graphical Processing Unit (GPU) computing is made. The detailed implementation steps of the PFDD-SAT are illustrated in this paper. Several experiments have been carried out to evaluate the detection time and accuracy and then the robustness to illumination and Gaussian noises. The results show that the PFDD-SAT has robustness to noise and speeds the defect detection process more than 200 times than normal required time and that greatly met the needs for real-time automatic fabric defect detection.

Fabric Defect Detection Using Wavelet-Enhanced Single-Point Photoelectric Sensing System

2012 ◽  
Vol 162 ◽  
pp. 497-504
Author(s):  
Shun Cong Zhong ◽  
Qiu Kun Zhang ◽  
Li Gang Yao ◽  
Yao Chun Shen
Keyword(s):  
Defect Detection ◽  
Light Transmission ◽  
Detection System ◽  
Single Point ◽  
Fluorescent Light ◽  
Fabric Defect Detection ◽  
Noise Rate ◽  
Fabric Defect ◽  
Negative Effect ◽  
Defect Signal

A fabric defect detection system based on single-point photoelectric sensing, was proposed and developed. The performance of the system was enhanced by using wavelet denoising algorithm to reduce the negative effect of noise and the environment disturb, such as fluorescent light in the factory. Instead of detection of light reflection from fabric, a light transmission technique with higher signal-to-noise rate was employed in the system for evaluating the fabric defect. A Si photodiode was used to record the defect signal of fabric. The signal was enhanced by stationary wavelet transform. The signal was quantitatively evaluated and finally a defect alarm was triggered. From the experimental results, the timeliness, robustness and detection precision of the system were demonstrated, therefore, it could be recommended for the applications in fabric industries.

scholarly journals Real-Time Fabric Defect Detection Using Accelerated Small-Scale Over-Completed Dictionary of Sparse Coding

10.5772/62058 ◽  
2016 ◽  
Vol 13 (1) ◽  
pp. 1 ◽  
Author(s):  
Tianpeng Feng ◽  
Lian Zou ◽  
Jia Yan ◽  
Wenxuan Shi ◽  
Yifeng Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Sparse Coding ◽  
Small Scale ◽  
Fabric Defect Detection ◽  
Fabric Defect
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