Mehmet TOPALBEKİROĞLU, L. Canan DÜLGER, H. İbrahim ÇELİK

Developing an algorithm for defect detection of denim fabric: Gabor filter method

Bu çalışmada, kumaş hata denetiminin otomatik olarak gerçekleştirilebilmesi için Gabor filtresi ve çift eşikleme metotları kullanarak bir algoritma geliştirilmektedir. Gabor filtresi ile yapılan konvolosyon işlemi sonucunda kumaş görüntüsündeki hatalı alan vurgulanırken hatasız doku sönükleştirilmektedir. Görüntü daha sonra çift eşikleme metodu kullanılarak ikili (binary) forma dönüştürülmektedir. Genişletme ve aşındırma morfolojik işlemleri kullanılarak gürültüler yok edilmekte ve hatalı alan belirgin bir şekilde ortaya çıkarılmaktadır. Hatanın sınırları işaretlenmektedir. Çözgü kaçığı, atkı kaçığı, delik, lekeli iplik ve düğüm gibi beş hata tipinden oluşan bir kumaş hata görüntüsü veritabanı oluşturulmuştur. Veritabanı her bir hata tipi ve hatasız kumaş numunesi için 30 farklı görüntü içermektedir. Böylece, algoritma 180 kumaş görüntüsüne uygulanmıştır, tüm hatalı alanlar yüksek başarı oranları ile tespit edilmiştir. Sonuçta kullanılan, algoritmanın başarısı istatistiksel olarak değerlendirilmiştir.

Denim kumaşın hata denetimi için bir algoritma geliştirilmesi: gabor filtre yöntemi

In this study, an algorithm is developed by using Gabor filtering and double thresholding methods for fabric defect detection automatically. The defective area of the fabric image is accentuated, and the defect-free texture is attenuated as result of convolution operation with the Gabor filter. The image is then converted into binary form for by using double thresholding method. The noises are removed and the defective area is determined clearly by using dilation and erosion morphological operations. The boundary of the defect is labeled. A fabric defect image database consists of five defect types; warp lacking, weft lacking, hole, soiled yarn and knot is formed. The database includes 30 different images for each type of defect and defect-free fabric samples. Thus, the algorithm is applied over 180 fabric images. All defective areas are detected with high success rates. The performance of the algorithm is given statistically.

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