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Journals / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2017 / Cilt: 23 - Sayı: 5

Wavelet transform and principal component analysis in fabric defect detection and classification

Kumaş hata tespiti ve sınıflandırmada dalgacık dönüşümü ve temel bileşen analizi

Kazım Yıldız
Ali Buldu
Pages
622–627
Publication Date
2017-10-20
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the experimental results, the fabric quality control process can be made after the drying and fixing, without any further quality control step.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Fabric defects are determined by quality control staff in textile industry.
📊
Key Findings & Data:Averagely 95% classification accuracy has been achieved on experiments for two different fabric types.
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Conclusion & Impact:According to the experimental results, the fabric quality control process can be made after the drying and fixing, without any further quality control step.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Termal görüntüleme · Kumaş hataları tespiti · Sınıflandırma

Abstract

Fabric defects are determined by quality control staff in textile industry. This process cannot be performed objectively and it constitutes both time and cost difficulties. In this study the cashmere and denim fabric images which are used often in textile industry are tried in both detection and classification process. Quality control machine prototype has been manufactured then defected fabric images were obtained with the help of thermal imaging. The fabric defects were detected and classified by using the thermal images. Averagely 95% classification accuracy has been achieved on experiments for two different fabric types. According to the experimental results, the fabric quality control process can be made after the drying and fixing, without any further quality control step.

Özet

Tekstil endüstrisinde kumaş hataları kalite kontrol elemanları tarafından tespit edilmektedir. Bu süreç hem objektif olmamakta hem de zaman ve maliyet sıkıntısı oluşturmaktadır. Bu çalışmada tekstil endüstrisinde sıklıkla kullanılan kaşe ve kot kumaştan elde edilen görüntüler üzerindeki hataların tespit edilmesi ve sınıflandırılması yapılmıştır. Kalite kontrol makinesi prototipi imal edilip termal görüntüleme yardımıyla hatalı kumaş görüntüleri elde edilmiştir. Termal görüntüler kullanılarak kumaş hataları tespit edilmiş ve sınıflandırılmıştır. İki farklı kumaş türü ile yapılan deneylerde ortalama %95 sınıflama başarısı elde edilmiştir. Deneysel sonuçlara göre kumaş kalite kontrol prosesinin, kumaş kurutma ve fiksleme işleminden sonra ilave bir kalite kontrol basamağı olmaksızın yapılabileceği ortaya konmuştur.

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Cite This Article

Yıldız, K., Buldu, A. (2017). Wavelet transform and principal component analysis in fabric defect detection and classification. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 23 - Sayı: 5, 622–627. https://ajindex.com/articles/42501

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