A thermal-based defect classification method in textile fabrics with K-nearest neighbor algorithm

Yildiz, Kazım; Buldu, Ali; Demetgül, Mustafa

doi:10.1177/1528083714555777

Cited by 40 publications

(19 citation statements)

References 19 publications

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“…The basic idea of the incremental property is to reuse the previous center points to obtain the next points, as shown in (11). Specifically, the value Discretized values are shown in (12), where n is the angle index, n  is the angle resolution,  is the number of angle values. First, the sin and cos are approximated, and then the angle values are rearranged to obtain the incremental property, as shown in (13).…”

Section: B Surface Defects Locationmentioning

confidence: 99%

“…In many studies, model input and parameter training are also the key research directions. Sun et al proposed a CNN-based network for adaptive multiscale image extraction, known as AMIC, which introduced pre-adoptive training on ImageNet [12]. Besides, Chang et al proposed a new deep model and extended an optimization signal design algorithm, which used reflected signals as model input instead of images [13].…”

Section: Introductionmentioning

confidence: 99%

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Automatic and Efficient Metallic Surface Defect Detection Based on Key Pixel Point Locations

Gao

Sun

et al. 2021

IEEE Sensors J.

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Surface defect detection aims to accurately recognize and distinguish types of defects and plays a key role in many applications. However, most of the recent studies focus on specific scenario detection and do not fairly consider the balance between the speed and accuracy. In the paper, we propose a key pixel points location-oriented method to identify multiscale defects, with several important properties: 1) A real-time template matching-based model is designed to speed up the process by introducing the Gaussian operator; 2) An improved Hough-based model is used to achieve a higher detection precision by deep mining both incremental properties and parallel properties; 3) An adaptive filtering-based image preprocessing method is proposed to eliminate the interference of multiple types of clutters and noises. In the experiments, a mean average rate of 96% was achieved to detect and classify four types of common defects and the average time was reduced to 0.149s. Furthermore, we fully evaluate the proposed method on two public datasets collected in real production lines and compare the results with other state-of-the-art methods. The results show that the proposed method achieved better balanced performance in many real application scenarios.

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Section: B Surface Defects Locationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic and Efficient Metallic Surface Defect Detection Based on Key Pixel Point Locations

Gao

Sun

et al. 2021

IEEE Sensors J.

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“…The image processing method has created a new branch in quality control and instrumentation; one can see that advanced imaging systems offered to the field for size assessment, calibration, transportation, production quality enhancement, inspection, grading, sorting, separation, and so on. Quality control issue is related to sampling of products, checkup of samples, and generalization of results to all products [4]. Determination of fabric properties and online controls such as control of web uniformity [5], defects [6], fiber diameter [7], and fiber orientation [8] are among the task that image processing systems can take over.…”

Section: Introductionmentioning

confidence: 99%

Measurement of the Uniformity of Thermally Bonded Points in Polypropylene Spunbonded Non-Wovens Using Image Processing and its Relationship With Their Tensile Properties

Emadi

Tavanaie

Payvandy

2018

Autex Research Journal

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This article aims at the image processing of surface uniformity and thermally bonded points uniformity in polypropylene spunbonded non-wovens. The investigated samples were at two different weights and three levels of non-uniformity. An image processing method based on the k-means clustering algorithm was applied to produce clustered images. The best clustering procedure was selected by using the lowest Davies-Bouldin index. The peak signal-to-noise ratio (PSNR) image quality evaluation method was used to choose the best binary image. Then, the non-woven surface uniformity was calculated using the quadrant method. The uniformity of thermally bonded points was calculated through an image processing method based on morphological operators. The relationships between the numerical outcomes and the empirical results of tensile tests were investigated. The results of image processing and tensile behavior showed that the surface uniformity and the uniformity of thermally bonded points have great impacts on tensile properties at the selected weights and non-uniformity levels. Thus, a sample with a higher level of uniformity and, consequently, more regular bonding points with further bonding percentage depicts the best tensile properties.

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“…For example, identification of user and usage profiles to innovate, create, and improve textile products and services 10,40 For example, predictive data analysis to understand user requirements in order to be able to design better textile products 46,47,56,57 • Recognition and classification of textile defects for quality control For example, fabric defects 6,8,38,51 (i.e., yarn, woven, knitted, dyeing defects), embroidery defects, 53 and garment defects 50,70 (i.e., cutting, sewing, and accessories defects)…”

Section: Advantages Of Dm Enabled In Textile Industrymentioning

confidence: 99%

“…The experiment material used in their study included 211 seam specimens from two kinds of fabrics. The classification performance of textile seam quality of 51 also used KNN algorithms to classify defects in textile fabrics via the obtained properties of feature-extracted images using a thermal camera. As a preprocessing step before applying DM, Yu et al 52 proposed a model for fabric hand prediction using fuzzy NN.…”

Section: K-nearest Neighbor In Textile Industrymentioning

confidence: 99%

Data mining and machine learning in textile industry

Yıldırım

Birant

Alpyıldız

2017

WIREs Data Min & Knowl

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Data mining has been proven useful for knowledge discovery in many areas, ranging from marketing to medical and from banking to education. This study focuses on data mining and machine learning in textile industry as applying them to textile data is considered an emerging interdisciplinary research field. Thus, data mining studies, including classification and clustering techniques and machine learning algorithms, implemented in textile industry were presented and explained in detail in this study to provide an overview of how clustering and classification techniques can be applied in the textile industry to deal with different problems where traditional methods are not useful. This article clearly shows that a classification technique has higher interest than a clustering technique in the textile industry. It also shows that the most commonly applied classification methods are artificial neural networks and support vector machines, and they generally provide high accuracy rates in the textile applications. For the clustering task of data mining, a K-means algorithm was generally implemented in textile studies among the others that were investigated in this article. We conclude with some remarks on the strength of the data mining techniques for textile industry, ways to overcome certain challenges, and offer some possible further research directions.

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A thermal-based defect classification method in textile fabrics with K-nearest neighbor algorithm

Cited by 40 publications

References 19 publications

Automatic and Efficient Metallic Surface Defect Detection Based on Key Pixel Point Locations

Automatic and Efficient Metallic Surface Defect Detection Based on Key Pixel Point Locations

Measurement of the Uniformity of Thermally Bonded Points in Polypropylene Spunbonded Non-Wovens Using Image Processing and its Relationship With Their Tensile Properties

Data mining and machine learning in textile industry

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