Fabric Inspection System Research Articles

Design of image classification system for fabric inspection process using Raspberry Pi

This research is designed as a prototype of defect inspection system on fabric production using machine learning-based image processing technology using the open source Google teachable machine application integrated with Raspberry Pi-3B. The prototype of fabric defect inspection system is built by utilizing two rollers that function as a fabric roll house before and after the inspection process. On both rollers, a fabric is stretched to be inspected, so that from a roll of fabric with a certain length, it can be seen how many defects occur on the fabric. The inspection system is carried out using a web camera with a certain level of lighting connected to a raspberry pi as a control device. Raspberry Pi functions as an image processing device and fabric rolling motor controller. In addition to the category of fabric in good condition, this system classifies into two categories of defects, namely slap defects and sparse defects. The test results show that this system has an average frame per second (FPS) of 4.85, an average inference time of 181.1 ms, with an accuracy of image classification results of 98.4 %.

Automated fabric inspection system development aided with convolutional autoencoder-based defect detection

Industrial automatic fabric inspection system, a critical technology in the industry, enhances both total production quantity and quality compared to conventional inspection techniques. This study aims to create a reliable and effective real-time automated visual inspection system for fabrics, focusing on defect detection. The goals of the study can be stated as; installing a system with advanced technology for capturing and processing images swiftly, the development and deployment of a system capable of autonomously learning and scanning fabrics in use, and the creation of a smart framework for accurate fabric defect detection and classification. We focus on the development of unsupervised fabric defect detection using a convolutional autoencoder model, and defect classification using a convolutional neural network model, which takes input as the feature vector generated by the convolutional autoencoder. The experimental outcomes have displayed significant success rates in both detecting defects and classifying them, confirming the effectiveness of the framework in real-time visual inspection systems.

Fabric defect detection using AI and machine learning for lean and automated manufacturing of acoustic panels

Fabric defects in the conventional manufacturing of acoustic panels are detected via manual visual inspections, which are prone to problems due to human errors. Implementing an automated fabric inspection system can improve productivity and increase product quality. In this work, advanced machine learning (ML) techniques for fabric defect detection are reviewed, and two deep learning (DL) models are developed using transfer learning based on pre-trained convolutional neural network (CNN) architectures. The dataset used for this work consists of 1800 images with six different classes, made up of one class of fabric in good condition and five classes of fabric defects. The model design process involves pre-processing of the images, modification of the neural network layers, as well as selection and optimisation of the network’s hyperparameters. The average accuracies of the two CNN models developed in this work, which used the GoogLeNet and the ResNet50 architectures, are 89.84% and 95.45%, respectively, showing statistically significant results. The interpretability of the models is discussed using the Grad-CAM technique. Relevant image acquisition hardware requirements are also put forward for integration with the detection software, which can enable successful deployment of the model for the automated fabric inspection.

Inspection of Cotton Woven Fabrics Produced by Ethiopian Textile Factories Through a Real-Time Vision-Based System

ABSTRACT Fabric is produced by the weaving process through the interlacement of warp and weft yarn or knitting process through the loop formation of yarn. During these processes, there is a possibility of fabric defect formation which hinders the acceptability by the fabric consumers. Ethiopian textile factories practiced a human inspection system, a traditional means of detecting fabric quality, for monitoring textile fabric defects. Manual fabric defect detection helps to instantly correct small defects, but it is time-consuming and results in human error due to fatigue and lack of concentration. Moreover, the accuracy of recognizing the defect highly depends on the mental status of the person that checks the defects. This initiated the development of a better fabric defect identification system that helps textile experts to detect fabric defects with better precision and speed. This study proposes a vision-based fabric inspection system for plain woven gray fabrics with a uniform texture. Accordingly, a comprehensive Fabric Defect Detection Database (FDDD) is constructed. The fabric significant features were calculated using a convolutional neural network (CNN) which is a state-of-the-art technology in image processing and task analysis. The experimental result of this study shows an average accuracy of about 89% in fabric defect recognition.

Fabric Fault and Extra Thread Detection using Convolutional Neural Network

A planar substance made of textile fibers is called fabric. The main reason why defective fabrics are produced is loom malfunctions. A specialized computer vision system called a fabric inspection system is used to find fabric flaws in order to ensure product quality. In this paper we classify the defect by using Convolutional Neural Network. Utilizing a special type of class-based ensemble convolutional neural network architecture, the defect recognition system is built. The experiment is carried out using several textile fiber kinds. There is four layers in CNN to classify the defect that is Convolution, ReLU, Pooling, Fully Connected layer. A number of well-known CNN architectures, such as Inception, ResNet, VGG, MobileNet, DenseNet, and Xception to classify the defect are tested in this study. Finally, The study demonstrates the result by classification and proves how accurately the defects are identified.

Application of K-Means Clustering to Enhance the Quality Control and the Impact of the Productivity in the Apparel Industries

Fabric inspection systems for measuring the quality are an essential part of producing best quality product in an apparel industry. The object of the research work is to analyze the acceptable fabric grade point based on the fabric grade points category using k-means clustering and their impact on the production. By applying the machine learning tools k-means clustering we categorized that hundred unsupervised data in three groups (A, B and C) based on acceptable grade points, of up to 15 (A), 15 to 30 (B), and 30 to 40 (C) respectively. The upper grade fabric can be consumed to produce better products. Thus, we can improve customer satisfaction as well as positive impact on the apparel manufacturing efficiency on both quality and quantity.

Qualitative classification of woven fabrics produced from recycled threads of cotton and blends

In this study, shirting fabrics were woven with weft threads produced from blends of recycled cotton fibers, original cotton fibers and original polyester fibers (100% original cotton, 100% recycled cotton, 35%/65% polyester/recycled cotton, 35%/65% polyester/original cotton, 50%/50% polyester/recycled cotton, 50%/50% polyester/original cotton). The produced fabrics were investigated in terms of fabric defects by using fabric quality control machines after wet treatment procedures have been carried out. The types and numbers of defects detected during quality control were recorded and graded according to Graniteville 78 Fabric Inspection System and were classified accordingly. All fabrics were evaluated in terms of fabric quality.

Detection And Classification of Weft Knitted Fabrics Defects Using Gabor Wavelet

The globalization of competition, the complexity of the economy and the plethora of information available today place companiesin a more than shifting context with which they must cope. Accelerating change becomes a constant feature of business life. Companies need methodological help to process a lot of information, In today's competitive world, customers are demandingbetter quality products with fast and reliable deliveries. To meet this demand, new manufacturing technologies are developingrapidly, resulting in new products and improvements in manufacturing processes.Today’s challenging world demands minimum loss and waste from industries. Moreover, it has to ensure the required quantityand quality with customer delivery lead time. A Circular weft knitting machine contains different parts such as needles, cams,sinkers, Fabric takedown mechanism, creel, a yarn metering and storage device, yarn breakage indicator, feeders and lubricationsystem. All those machine parts are responsible to increase or decrease the productivity of weft knit fabric production as well asthe fabric quality. The Circular weft Knitting Machine has to stop when defects occurred and then faults are corrected, whichresults in a loss in time and efficiency in order to be ready to meet customer requirements; the goal is to quickly provide productsthat combine quality and competitive price. In this sense, effective monitoring is required to avoid defects and maintain highproductivity and customer required quality. The purpose of this study is to identify and analyze weft knitted fabric defects onthe weft circular knitting machine of knitting industries.This paper describes a computer vision-based fabric inspection system implemented on a weft circular knitting machine to detectdefection and classification of the weft-knitted fabric defects under construction. We using Gabor Wavelets that have been successfully applied to various machine vision applications such as Texture segmentation, Edge detection, and Boundary detection, a multi-scale and multi orientation Gabor filter scheme simulates the human eye and that’s applied to the weft-knittedfabric under construction. On-line weft knitted fabric defect detection was tested automatically by analyzing fabric imagescaptured by a digital camera using Gabor wavelets and classification (Identification) these fabric defects to known classes. Wesucceeded to detect weft knitted fabric defects and classify defect’s on the weft circular knitting machine at the same time themachine stops to correct the fabric defect to achieve customer required quantity and quality. As well as we cancel the fabricinspection process that means, saving money, time, manpower which leads to reducing production lead time and cost.

Development of an Online Automated Fabric Inspection System

In this work, the development of a system for the automated inspection of mesh fabrics in real time through computer vision techniques is described. Here, a processing pipeline that includes a distortion removal procedure, a texture-based detector, a shape and an artificial neural network classifier is proposed. Different from other works found in the literature, the developed system was evaluated under real inspection conditions. The obtained results show that the proposed system not only achieves compatible defect detection and classification rates than the current state-of-the-art methods but also met functional and non-functional requirements observed in industry.

REAL-TIME DETECTION OF KNITTING FABRIC DEFECTS USING SHEARLET TRANSFORM

This paper proposes a vision-based
 fabric inspection system for the circular knitting machine. Firstly, a
 comprehensive fabric database called Fabric Defect Detection Database (FDDD) are
 constructed. To extract significant features of fabric images, shearlet
 transform is used. Means and variances are calculated from all subbands and
 combined into a high-dimensional feature vector. The proposed system is evaluated
 on a circular knitting machine in a textile factory. The real-time performance
 analysis is only carried out by inspecting single jersey knitted fabric. Our
 proposed system achieves the highest accuracy of 94.0% in the detection of single
 jersey knitting fabric defects.

Fabric Defect Detection Using Activation Layer Embedded Convolutional Neural Network

Loom malfunctions are the main cause of faulty fabric production. A fabric inspection system is a specialized computer vision system used to detect fabric defects for quality assurance. In this paper, a deep-learning algorithm was developed for an on-loom fabric defect inspection system by combining the techniques of image pre-processing, fabric motif determination, candidate defect map generation, and convolutional neural networks (CNNs). A novel pairwise-potential activation layer was introduced to a CNN, leading to high accuracy of defect segmentation on fabrics with intricate features and imbalanced dataset. The average precision and recall of detecting defects in the existing images reached, respectively, over 90% and 80% at the pixel level and the accuracy on counting the number of defects from a publicly available dataset exceeded 98%.

An Empirical Method for Optimization of Counterpropagation Neural Network Classifier Design for Fabric Defect Inspection

Automated, i.e. machine vision based fabric defect inspection systems have been drawing plenty of attention of the researchers in order to replace manual inspection. Two difficult problems are mainly posed by automated fabric defect inspection systems. They are defect detection and defect classification. Counterpropagation neural network (CPN) is a robust classifier and very promising for defect classification. In general, works reported to date have claimed varying level of successes in detection and classification of different types of defects through CPN; but in particular, no claimed has been made for successful application of CPN for fabric defects detection and classification. In those published works, no investigation has been reported regarding to the variation of major performance parameters of NN based classifiers such as learning time and classification accuracy based on network topology and training parameters. As a result, application engineer has little or no guidance to take design decisions for reaching to optimum structure of NN based defect classifiers in general and CPN based in particular. Our work focuses on empirical investigation of interrelationship between design parameters and performance of CPN based classifier for fabric defect classification. It is believed that such work will be laying the ground to empower application engineers to decide about optimum values of design parameters for realizing most appropriate CPN based classifier.

Automated Fabric Defect Inspection: A Survey of Classifiers

Quality control at each stage of production in textile industry has become a key factor to retaining the existence in the highly competitive global market. Problems of manual fabric defect inspection are lack of accuracy and high time consumption, where early and accurate fabric defect detection is a significant phase of quality control. Computer vision based, i.e. automated fabric defect inspection systems are thought by many researchers of different countries to be very useful to resolve these problems. There are two major challenges to be resolved to attain a successful automated fabric defect inspection system. They are defect detection and defect classification. In this work, we discuss different techniques used for automated fabric defect classification, then show a survey of classifiers used in automated fabric defect inspection systems, and finally, compare these classifiers by using performance metrics. This work is expected to be very useful for the researchers in the area of automated fabric defect inspection to understand and evaluate the many potential options in this field.

An Empirical Approach to Optimize Design of Backpropagation Neural Network Classifier for Textile Defect Inspection

Automated fabric inspection systems have been drawing plenty of attention of the researchers in order to replace manual inspection. Two difficult problems are mainl y posed by automated fabric inspection systems. They are defect detection and defect classification. Backpropagation is a popular learning algorithm and very promising for defect classification. In general, works reported to date have claimed varying level of successes in detection and classification of different types of defects through backpropagation model. In those published works, no investigation has been reported regarding to the variation of major performance parameters of neural network (NN) based classifiers such as training time and classification accuracy based on network topology and training parameters. As a result, application engineer has little or no guidance to take design decisions for reaching to optimum structure of NN based defect classifiers in general and backpropagation based in particular. Our work focuses on empirical investigation of interrelationship between design parameters and performance of backpropagation based classifier for textile defect classification. It is believed that such work will be laying the ground to empower application engineers to decide about optimum values of design parameters for realizing most appropriate backpropagation based classifier.

Design Automatic Fabric Defect Inspection System

The system with four pieces of DSP + FPGA and procesing the digital image information collected with the field reprogrammable array FPGA has been designed, it can realize the automatic detection for the fabric defect. The system make use of four pieces of TMS320C62x as the core to process the digital image information; The FPGA realizes the automatic detection system which controls the interconnect fabric defect. The program based on attributed relational histogram and support vector machin has been designed. The experiment show when the velocity of sample fabric is of 100m/s and 120m/s the rate of recognize nicely with which theautomatic fabric defect inspection system being provided can achieve 80% and 60%.

Distinguishing Feature Selection for Fabric Defect Classification Using Neural Network

Over the years significant research has been performed for machine vision based fabric inspection systems in order to replace manual inspection, which is time consuming and not accurate enough. Automated fabric inspection systems mainly involve two challenging problems: one is defect detection and another is classification, which remains elusive despite considerable research effort in automated fabric inspection. The research reported to date to solve the defect classification problem appears to be insufficient, particularly in selecting appropriate set of features. Scene analysis and feature selection play a very important role in the classification process. Insufficient scene analysis results in an inappropriate set of features. Selection of an inappropriate feature set increases complexities of subsequent steps and makes the classification task harder. Considering this observation, we present a possibly appropriate feature set in order to address the problem of fabric defect classification using neural network (NN). We justify the features from the point of view of distinguishing quality and feature extraction difficulty. We performed some experiments in order to show the utility of proposed features and compare performances with recently reported relevant works. More than 98% classification accuracy has been found, which appears to be very promising.

レーザスペックルによる織物構造と欠陥の計測

The fabric inspection is indispensable for evaluating the quality of textiles. However it has been performed by the visual evaluation. The visual inspection is a burden to the operator, and it is also difficult to evaluate qualitatively and quantitatively. Automation and labor saving are desired to improve uniformity and quality. Therefore, in this paper we examined the possibility of the fabric inspection system, which is based on analyzing the autocorrelation function obtained from the speckle pattern of textiles. And the possibility of the quantitative measurement of weave, yarn density, defect, and yarn count was obtained by analyzing the autocorrelation function obtained from the speckle pattern of the textile surface.

Development of Real-Time Vision-Based Fabric Inspection System

Quality inspection of textile fabric products is an important problem for fabric manufacturers. This paper presents an automatic vision-based system for the quality control of web textile fabrics. Typical web material is 1-3 m wide and is driven with speeds ranging from 20 to 200 m/min. At present, the quality inspection process is manually performed by experts. However, they cannot detect more than 60% of the overall defects for the fabric if it is moving faster than 30 m/min. To increase the quality and homogeneity of fabrics, an automated visual inspection system is needed for better productivity. Currently, the existing inspection systems are too expensive for small companies. In this paper, a PC-based real-time inspection system is proposed with benefits of low cost and high detection rate. The proposed algorithm showed good results for several types of fabric defects.

Computer Vision-Aided Fabric Inspection System for On-Circular Knitting Machine

This paper describes a computer vision-based fabric inspection system implemented on a circular knitting machine to inspect the fabric under construction. The study consisted of two parts. In the first part, detection of defects in knitted fabric was performed and the performance of three different spectral methods, namely the discrete Fourier transform, the wavelet and the Gabor transforms were evaluated off-line. In the second part, knitted fabric defect-detection and classification was implemented on-line. The captured images were subjected to a defect-detection algorithm, which was based on the concepts of the Gabor wavelet transform, and a neural network (as a classifier). An operator encountering defects also evaluated the performance of the system. The fabric images were broadly classified into seven main categories as well as seven combined defects. The results of the designed system were compared with those of human vision.

Detecting Defects in Weft-knitted Fabrics Using Texture-Recognition Methods

This paper describes fabric inspection system aided by computer vision to detect and classify defects in circular knitted fabrics using different common texture-recognition methods, including co-occurrence matrices, the discrete Fourier transform, wavelets, Gabor, and clustering. The images of the fabrics were broadly classified into six classes: cracks, holes, vertical stripes, horizontal stripes, soil freckles, and defect-free. One hundred and twenty images (256 gray level and 100 dpi) containing 20 images of defect-free fabrics (rib 1x1) as well as 100 images corresponding to five different categories were used. In general, one-half of the images in each category were employed for training and the remaining images were used for testing. The application of the clustering method applied in this work was found to be highly promising at identifying defects in knitted fabrics. With an overall success rate of 91.6%, the clustering method has a higher efficiency value than all of the other methods. In the case of the wavelet and Gabor methods, the results are acceptable. However, the overall success rates of the co-occurrence matrix and Fourier transform methods in recognizing defects in knitted fabrics are not acceptable.