(REFEREED RESEARCH) DETERMINING THE WOVEN FABRIC DEFECTS BY IMPLEMENTING IMAGE COMPARISON METHODS GÖRÜNTÜ KARŞILAŞTIRMA METODU İLE DOKUMA KUMAŞ HATALARININ TESPİTİ Cihat Okan ARIKAN 1, Hüseyin KADOĞLU 2 * 1 Ege University, Emel Akın Vocational School, İzmir, Turkey 2 Ege University, Department of Textile Engineering, İzmir, Turkey Received: 06.06.2013 Accepted: 20.10.2013 ABSTRACT Various fabric defects may occur over the surface structure during the production or use of the woven fabrics. Detection of the defects over the surface of the fabric is one of the most important factors for assessing the quality of the fabric. Fault detection is generally made by the human eye and the inspection of the fabric is a very laborious operation. In order to simplify this operation, the computer-assisted image processing methods may be used. In such methods, fabric image is electronically captured by appropriate camera systems which provides a faster comparison and also eliminates the human perceptive biases. In this study, solid-colored woven fabrics were subjected to the several image processing methods in order to detect certain fabric defects. Key Words: Woven fabrics, Image processing, Error detection, Fabric defects. ÖZET Dokuma kumaşlarda, kumaş yüzeyinde meydana gelen hatalar, kumaşın gerek üretimi sırasındaki yapısal hatalardan, gerekse kullanımı sırasında oluşan hatalardan oluşur. Kumaş yüzeyindeki hataların tespit edilmesi de kumaşın kalitesini belirlerken dikkat edilmesi gereken önemli bir faktördür. Kumaş hatalarının tespiti genellikle göz ile incelenmesi yoluyla yapıldığından oldukça zahmetli bir işlemdir. Bu işlemi basitleştirmek amacıyla, bilgisayar destekli görüntü işleme metodları yoğun olarak kullanılmaya çalışılmaktadır. Kumaş görüntüsünün elektronik kameralar ile sürekli kontrol edilerek, hem daha hızlı hem de insana bağımlı olmaktan kurtarması nedeniyle kumaş hatalarının tespitinde oldukça uygun bir yöntemdir. Bu çalışmada düz renkli dokuma kumaşlarda, görüntü işleme metodunun hata tespiti için kullanımına yönelik araştırmalar yapılmıştır. Anahtar Kelimeler: Dokuma kumaş, Görüntü işleme, Hata tespiti, Kumaş hataları. Corresponding Author: Cihat Okan Arıkan, cihat.arikan@ege.edu.tr, Tel: +90 232 311 27 73, +90 232 311 33 39 1. INTRODUCTION Textile machines currently reached quite higher operation speeds and efficiency levels. Especially yarn spinning machinery is capable of producing high volumes at shorter time frames in line with the developments in the automation systems. The increased production capacity induced several problems for the mills which accommodate quality control departments that utilizing human eye for defect inspectione.g. failure to detect the fabric defects or necessity for employing increased number of workers in order to inspect all of the fabrics. Because of this, being capable of measuring the quality during the production has great importance for any producer as this will allow to reduce the quality control costs and increase the production efficiency. During the recent years in line with the developments at the computer technology, both software and especially optic-electronic hardware, numerical image analysis evolved as a contemporary research field (1). Chan ve Pang (2), studied the woven fabric defects over warp direction by comparing the spectral images of both defected and non-defected fabric samples. In this study it was observed that the Fourier analyses may have been employed for detecting fabric defects. The outcomes of the study revealed that it was possible to detect the combined spectral image of the normalized dimension belonged to a broken yarn both in weft direction and warp direction via utilizing upshifting at spectrum. This upshifting occurs because of the increased amount of light penetrating through the fabric which comes about by the left over emptiness by the broken yarn. It is TEKSTİL ve KONFEKSİYON 23(4), 2013 325
possible to detect any existing double yarn via utilizing downshifting from the original image over to the defect accommodating image at the spectrum. Thus it is possible to detect punctures or holes, and similar two dimensional defects over the fabric both at warp and weft directions. Atmaca (3), carried out studies aiming to detect and classify the fundamental fabric defects encountered over knitted fabrics by utilizing Fourier analysis, histogram equalization and median filtering image processing methods. The details of fabric images were enhanced via histogram equalization. Median filtering were utilized to remove any possible noise from the images. The dimensional power spectrums were derived in accordance with the periodic structure of the fabric via Fourier analysis and the grain direction of the fabric was determined by utilizing these data. By inspecting these spectrums, it was determined the qualitative aspects for assigning the defects in related groups. Artificial neural network methods were also utilized together with image processing methods for being able to classify the results. The algorithms which were developed by this study were not applied for various diverse fabric parameters such yarn count, yarn density and fabric pattern. Arı (4), inspected the creases over various textile fabric surfaces via implementing image analysis methods. In this study the crease ratings and the crease resistance values were measured by applying frequency analysis in computer environment. 12 pieces of fabric samples having different raw materials and patterns were induced to creasing process in accordance with TS EN 390 standard and then their crease angles were measured. The images of the samples were captured via camera and transferred to computer environment. These images were converted to pixels and each of these pixels was assigned a numerical value in accordance with its gray scale enumeration within the range of 0-255 in order to set up a matrix. Then, the aforementioned matrix was assessed via frequency technique analysis methods and the outcomes were interpreted regarding the raw material and pattern structure of each fabric sample. The statistical analysis revealed that obtained results were significantly reliable for different patterns providing that the raw material was kept identical and also reliable for different raw materials providing that the pattern was kept identical. Yılmaz (5) pointed out the viability of image analysis methods for designing security systems and motion analysis applications. In this study, the motion analyses were examined by background differentiation methods along with statistical tools. In this study it was observed that the outdoor images were severely influenced by light intensity variations which interfered as a substantial impeding factor. For indoor images it was possible to control the ambient light intensity via fixed light sources whereas such a compensation for outdoor environment was impossible which eventually deteriorated the outdoor trials. Besides, it evolved as a necessary to clean out the noise which occurred over the differentiation images during the processing. This necessity rendered the applied algorithm longer requiring extended processing times. Torun (6), studied on a real time defect interception system which can be utilized on circular knitting machines for textile industry. In this study, by way of processing the real time images in computer environment which were captured through a knitting machine mounted camera, it was achieved to detect some types of knitted fabric defects. The system managed to perceive defects over the knitted fabric on a Fouquet brand double plates, circular knitting machine just after 10 cm onwards the occurrence of the defect. For being able to perceive the image of exposure zone clearly, a ring shaped light source with bright LED lamps was designed to illuminate the experiment zone. After analyzing the captured images, it was observed that various defects such as holes, horizontal and vertical oil stains, needle breaks, colored yarns, thick yarns, thin yarns, fabric drops were sensed successfully. At this study, it was also achieved to set threshold boundaries for the above mentioned defects in order to stop the knitting machine if a preset range was transgressed. Jeong (7), investigated how the image processing methods to be utilized for ascertaining the warp yarn direction and the weft yarn direction. Several fabric images were acquired via a scanner and these images were subjected to various filters which revealed the validity of such a method for recognizing warp yarns and weft yarns. In this study, density estimation method (similar to Fourier analysis) was implemented. Within the context of the study, several trials were carried out for identifying the warp-weft directions over the skewed fabrics with the angle of 45 degree. The images were subjected to threshold phase filtering and then 2-D spatial gradients of the images were obtained via Sobel operator method. Hough transform was applied for deciding which lines and columns to be used. Ala (8), utilized image processing methods for digitalization of woven fabric defects in computer environment. In this study, the viability of recognizing the fabric defects by digitalization of them through several image processing methods and subsequently subjecting them to various fitters was explored. This approached was enabled several defects, especially the warp yarn related ones to be detected. Within the context of the study it was also aimed to achieve various textile related evaluations such as fabric drapeability and yarn diameter calculations via image processing utilization. Image comparison, as the designation resonates, is utilized by comparing two images and revealing the the differences between them. It is possible to compare two still images or to compare a still image with any captured one simultaneously which are being obtained via a computer connected camera. This method is developed with the aim of detecting the defects over the surface of fabric via comparing a base still fabric image with a moving fabric. The same method is also capable of detecting and even differentiating to some degree certain defects such as thick places and neps over textile yarns. Most of the defects which occur over the woven fabrics include the holes and nodes because of broken warp and filling yarns, and stains because of oil or paint drops. The weaving 326 TEKSTİL ve KONFEKSİYON 23(4), 2013
machine related defects evolve as structural deformations (pattern failure, pucking, hole etc.) over the fabric surface. While passing through the quality control table, the dimension and the velocity of the fabric render the visual inspection more difficult which eventually increases the probability of missing the existing defects. It is required more accurate and efficient methods for detecting the fabric defects as it is quite possible for human eye to fail to perceive a considerable amount of fabric defects via visual inspection. Consequently, the necessity and importance of automated defect detection in textile industry tend to increase at a steep pace. Automated fabric inspection yields higher accuracy standards than visual inspection hence enabling the producer to save money and time. However most of the automated systems are only capable of inspecting the manufactured fabrics in the off line mode (ex-machine status). Actually the most effective inspection method is the one which monitors the yarns or fabrics simultaneously while just being produced (10). 2. MATERIAL AND METHOD 2.1. Material In this study, it is utilized a TV card with a BT878 chip having 640x480 resolution capacity for image capturing in conjunction with a Sony CCD- TVR208E camera having 800x600 maximum resolution which is capable of automatic/manuel shutter time setting for acquiring the images. The fabric samples which are subjected to the comparison trials were obtained from Ege University Textile Engineering Department and Ege University Textile and Apparel Research & Application Center. These are selected from the single colored fabrics which have been classified previously as second quality because they were accommodating various fabric defects. 2.2. Method For being able to achieve the image comparison operations in computer environment, an appropriate software was developed exclusively by using Borland Delphi Developer Studio (BDS) 2006 programming language. The aforementioned software was actually capable to implement various image analysis methods such as compare two images pixel by pixel; saving the colors of all pixels and compare different image for these values; calculating the average color map value and comparing all the captured images with this reference value. However, for this study it is especially improved to focus on background differentiation method for detecting the fabric defects which is the part of comparing image areas pixel by pixel on the surface. Since the calculating these values requires very high mathematical GPU processes while the fabric flows front of the camera, the process requires very special image capturing cards (take 200 frames per second) and hardware for the expected results. In this study, capturing card is a standart VGA camera for low resolution images, so the fabric complexity is set very low to get the simple test results. The background zones are the ones which basically remain unmodified and keep the same form over the acquired images. During the motion analyses, it is achieved to determine the differentiating zones and by this way the detection of the defects via isolating the continuously identical zones. It requires to have a basic (which is rated as flawless) image beforehand for being able to differentiate the background during the image analyses. Only after acquiring such a basic image it is possible to remove the identical zones from the compared images which consequently allow ascertaining any differentiating zones. In Figure 1, the image of (a) which is rated as being a flawless fabric surface and compared to the image of (b) which is rated as being a defective woven surface (12). In the image of (c), only the differentiating zone is detected between two images. However, for the cases which were not possible to see clearly the black zone, the image of (d) was acquired via transforming the differentiation image to negative state. Figure 1. Detection of the differentiating zone via background isolation method TEKSTİL ve KONFEKSİYON 23(4), 2013 327
It is required to establish the basic reference image (The original image page) and also the images which would be subjected to comparison (The comparison image page). The original image must be an image which was captured from the flawless zone of the fabric surface. The comparison image can be either a still image which was captured form any other zone of the fabric or an instant image which is captured directly by the camera simultaneously. When two images to be compared are selected, the differentiating zones between two images will be displayed on the Image differentiation page. At Figure 2 it is displayed the fabric image accommodating a loose warp defect as an example and at Figure 3 it is displayed the differentiating image obtained via comparison. At the image page, the identical pixels over both of the images are converted in to the black color and the differentiation image displays only the differentiated zones. Consequently, it is achieved to reveal the defective zones which induce differentiation over the surface of fabric. Figure 2. Image comparison screen Figure 3. The differentiating image obtained via comparison Figure 4. The settings which are used for image comparison analysis 328 TEKSTİL ve KONFEKSİYON 23(4), 2013
The differentiation image which is acquired via comparison analysis is an 8-bit gray scale image. Color shade related fabric defects usually show themselves as pale colored zones over the differentiation image. In case of insufficient visible details, please select Negatifini göster (Display as negative) option from as shown at Figure 4 for being able to review the negative image to explore an another approach. In the negative image state, the details within the dark zones will reveal themselves clearer. If the image dimensions happen to be extremely large, please select the Ekrana sığdır (Fit to screen) option for being able to review the image at full width on the parent window dimensions. The equivalency ratios for the compared images are displayed at the bottom of the screen as percentiles (%). For instance, "0.58% difference" statement means there is a 0.58% equivalency difference between two images. This value has been calculated by automatically by using pixel comparison method according to the reference image area and it shows that in 1000 pixels on the captured surface, 58 pixels are identifically not equal for compared images (reference image and the captured image). The calculated value is very small (little than 1) so the difference between two fabric is not important at this point. This difference usually varies in accordance with the dimension and coverage of the defect. For defects such as holes or ruptures this ratio may reach up to 10-15% level, for warp or weft yarn related defects this ratio dwells around 1-8% level. These values vary by the fabric type, light density on the area, colors on the fabrics etc.. So, it must be determine the best value by capturing images about 5-10 meters and check the value for fabric surface (mostly without any defect). The level should be accept as non-defect value and process will continue with that value. In this study, according to the fabric surface, the values are determined as 0-2% has no errors, 2-4% faint errors, 5-8% evident fabric defects and 8% and above are very big surface defects. It is possible to specify any value exceeding the predetermined range as a defect. It is also possible to save any detected differentiating image in a specified folder for reviewing later by selecting Hata oluştuğunda görüntüyü klasöre kaydet (Save the image to folder) when a defect is occurred option from the right side of the settings tab. 3. RESULTS AND DISCUSSIONS The implemented image processing methods within the context of this study enables to detect various fabric defects. The experimental setting is based on basic patterned and solid colored defective fabric samples. The operational state of the system requires appropriate software but also the spinning and weaving machines require to be structurally modified for achieving an integrable outfit. During the analyses of yarn and fabric defects, the resolution level and image capturing speed of the camera fell behind the required range and the vibrations which are induced by the dynamic forces of the weaving machine rendered the superstructure of the camera carrier unoperational for capturing full width fabric images. Because of these factors, this study was carried on by using several images which were captured over the fabric samples. Numerous comparisons were employed between basic flawless fabric images and defective fabric images and when a certain degree of differentiation was detected exceeding the preset values, such a detection was specified as an indication of a fabric defect. The technical limitations of the available camera and image capturing devices restrained the scope of the study significantly. Nevertheless it is observed that it is possible to capture some sort of fabric defects to a certain degree even with a simple optic system. Thus it is quite possible to suggest that especially sophisticated optic-electronic systems have great potential for the automation of fabric defect detection operations. Therefore this study conveys experimental data for the viability of such systems. It is quite possible to manufacture compact and versatile computer aided opticelectronic defect detection devices by implementing advanced image analysis methods and algorithms. The results of this study may propose a functional interim stage for future researches. REFERENCES 1. Akyol, B. Ö., 1999, Sayısal Görüntü İşlemede Görüntü Karşılaştırma, Gazi Üniversitesi, Y.L. Tezi. 2. Chan, C. and Pang, G., 2000, Fabric defect detection by Fourier analysis, IEEE Transactions on Industry Applications, 36(5):1267-1276. 3. Atmaca, V., 2005, Örme Kumaşlardaki Üretim Hatalarının Görüntü İşleme Teknikleri ile Otomatik Tespiti ve Sınıflandırılması, İstanbul Teknik Üniversitesi, Y.L. Tezi. 4. 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