Fatma Günseli YAŞAR ÇIKLAÇANDIR, SEMİH UTKU, HAKAN ÖZDEMİR

KUMAŞLARDA HATAYI YEREL OLARAK ARAYAN DENETİMSİZ BİR SİSTEM

Kumaşlarda dokuma sırasında veya sonrasında oluşan kusurlar kumaşların kalitesini düşürür. Teknolojinin gelişmesiylebirlikte, kumaşlarda görülen kusurların sıklığı azalmıştır, ancak yine de ortaya çıkmaktadır. Kalite kontrol amacıyla kumaşlardakikusurların tespiti bir personel tarafından yapılmaya çalışılır. Bu işlem yorucu olduğu kadar zordur. Ayrıca, kişinin hata yapmaolasılığı çok yüksektir. Bu kontrol sürecini otomatikleştirmek için pek çok çalışma yapılmıştır. Bu çalışmada, kumaşın herhangi birbölgesinde hata olup olmadığını bulmak için kumaş görüntüsü eşit boyutlarda bloklara ayrılmıştır. Görüntüye ait her bir bloğa özellikçıkarma metodu uygulanıp çıkarılan bu özellikler K-means kümeleme algoritmasına sokulmuştur. Çalışmada özellik çıkarımı için griseviye eş oluşum matrisi ve ortanca (medyan) farkı olmak üzere iki farklı metot uygulanıp performansları karşılaştırılmıştır. Yapılandeney sonuçlarına göre, gri seviye eş oluşum matrisi kullanıldığında hatayı bulma başarısının %97.99’a kadar yükseldiğigözlemlenmiştir. Medyan farkı kullanılarak kümeleme yapıldığında ise başarı oranı %86.91’e kadar çıkmaktadır. Ayrıca, başarılaratkı yönündeki hatalar ve çözgü yönündeki hatalar için ayrı ayrı hesaplandığında atkı yönündeki hataların çözgü yönündeki hatalarakıyasla daha kolay bulunduğu sonucuna varılmıştır.

AN UNSUPERVISED SYSTEM LOCALLY SEEKING FABRIC DEFECTS

Defects in the fabrics during or after weaving reduce the quality of them. With the development of technology, the frequency of the defects seen in fabrics has decreased, but still occurs. In the process of detecting fabric defects, the quality control unit tries to detect fabric defects. This process is both personal and time consuming, leading to costly and personal errors. For this reason, solutions have been proposed in studies to carry out and automate the process under computer control. In this study, fabric images are divided into blocks of equal sizes to find out whether there are any defects in the fabrics. The features, which are extracted by applying feature extraction method to each block of the image, are inserted into the K-means clustering algorithm. Two different methods are applied for feature extraction (gray level co-formation matrix and median difference) and their performances have been compared. The success rate of detecting the defect increases up to 97.99% when the gray level co-occurrence matrix is used. The success rate of detecting the defect increases up to 86.91% when the median differences are used. In addition, In addition, when the success rates are calculated separately for the defects in the weft direction and the defects in the warp direction, it is concluded that the defects in the weft direction are easier to find than the defects in the warp direction.

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