Application of Machine Vision Techniques in Textile (Fabric) Quality Analysis

Kaur, Navneet; Dalal, Mandeep

doi:10.9790/3021-0204582584

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…e tonal defect changes the local intensity value. Kaur et al [26] projected the Gabor technique to address the faulted texture by using digital image processing techniques. Colin Sc Tsang and his team represent a novel Elo rating method for fabric inspection from the uniform background of the fabric.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Woven Fabric Faulty Images Using Convolution Neural Network

Ashraf

Ijaz

Asif

et al. 2022

Mathematical Problems in Engineering

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Convolution neural network (CNN) is one of the most popular machine learning techniques that is being used in many applications like image classification, image analysis, textile archives, object recognition, and many more. In the textile industry, the classification of defective and nondefective fabric is an essential and necessary step to control the quality of fabric. Traditionally, a user physically inspects and classifies the fabric, which is an ineffective and tedious activity. Therefore, it is desirable to have an automated system for detecting defects in the fabric. To address these issues, this research proposes a solution for classifying defective and nondefective fabric using deep learning-based framework. Therefore, in this research, an image processing technique with CNN-based GoogleNet is presented to classify defective and nondefective fabric. To achieve the purpose, the system is trained using different kinds of fabric defects. The performance of the proposed approach was evaluated on the textile texture TILDA dataset, and achieved a classification accuracy of 94.46%. The classification results show that the proposed approach for classifying defective and nondefective fabric is better as compared to other state-of-the-art approaches such as Bayesian, BPNN, and SVM.

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Section: Literature Reviewmentioning

confidence: 99%

Classification of Woven Fabric Faulty Images Using Convolution Neural Network

Ashraf

Ijaz

Asif

et al. 2022

Mathematical Problems in Engineering

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“…A Visual Inspection System to detect and classify rolled steel defects using methods like Computer Vision techniques and Artificial Neural Network. Defects like welding, clamp and identification hole were classified using Hough Transform [8]. In Future, the segmentation process will be done based on an effective segmentation approach for effective partitioning a defect region from other parts [9].…”

Section: Literature Reviewmentioning

confidence: 99%

A New Research on Identify Defects in Stainless Steel Plate Using Image Processing

2019

IJITEE

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Quality management is very challenging in the production of steel plates. Identifying the defects in surface of steel plates is highly challenging and plays a vital role in high percentage and in quality control process also. In most of the production fields, only manual way of checking the defects is done. Due to which the following constraints are faced. Less efficiency, increase in production cost etc.. In industries, an automated technique which provides better detection, recognition and classification as well is used. Image processing identifies the defect and classifies using MATLAB.

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“…In this part, the existing fabric defect detection has been discussed and it is a popular research topic [8,9] for many years. Navneet Kaur [10] presented a Gabor filter method for textile fault detection problem. It was chosen as a appropriate delegate of this class of schemes.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Fabric Defect Prediction Based on Modular Neural Network Classifier with Alternative Hard C-Means Clustering

Rathinavel¹,

Kannaianl²

2018

IJET

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In India, textile industry has been mainly focused because it increased the economy day by day. But, it has some problem in the field of quality control. At present, it is mainly solved visually through skilled workers. Though, due to the human errors and eye fatigue, the system reliability has been restricted. So, in this research has been focused automatic fabric defect detection scheme. Here, Modular Neural Network (MNN) is proposed for fabric defect detection and classification with low cost and high accurate rate via using image processing schemes in the woven fabrics. At first, the images are collected from the machine and then preprocessed by using E nhanced Directional Switching Median Filter (EDWF) to reduce the impulse and stationary noise. To attain high accurate prediction, the preprocessed image has been segmented by using Alternative Hard C-Means (AHCM) cluster. After clustering, the images are converted to binary image. Then, the first order features has been extracted from the image. The extracted features are given as input to MNN, which classifies the fabric defects. In MNN, the weight factors are calculated by using back propagation algorithm and genera te the output. The simulation results show that the proposed MNN attained high accuracy rate of 96.7% when compared to existing Artificial Neural Network (ANN) than Support Vector Machine with Genetic Algorithm (SVM-GA) classification algorithms.

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Application of Machine Vision Techniques in Textile (Fabric) Quality Analysis

Abstract: ABSTRACT

Cited by 5 publications

References 7 publications

Classification of Woven Fabric Faulty Images Using Convolution Neural Network

Classification of Woven Fabric Faulty Images Using Convolution Neural Network

A New Research on Identify Defects in Stainless Steel Plate Using Image Processing

An Efficient Fabric Defect Prediction Based on Modular Neural Network Classifier with Alternative Hard C-Means Clustering

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