Comparison of Deep Learning Architectures for Fabric Defect Detection in Textile Production Lines and a New Image Dataset

Abstract

Quality control processes in the textile industry rely heavily on human inspection, which results in significant limitations such as high costs, low speed, and the risk of human error. In particular, it is difficult to detect fabric defects at the millimeter level manually, which can lead to defective products passing through the production line. In this study, an automated fabric defect assessment system based on image-level binary classification was developed, aiming to separate fabric images obtained from textile production lines into defective and defect-free categories. A new dataset consisting of 2,915 images with a resolution of 512×512 was created. Twenty percent of the dataset was reserved as an independent test set, while 10% of the remaining 80% development set was used for validation. Five different deep learning architectures, namely Original CNN, VGG16, ResNet50, InceptionV3, and MobileNetV2, were comparatively analyzed. Experimental results demonstrated that the MobileNetV2 model achieved the highest numerical performance values—99.31% accuracy, 99.32% precision, 99.31% recall, and a 99.31% F1-score—using the existing fixed data split. Furthermore, MobileNetV2's low-parameter and computationally lightweight architecture offers a potential advantage for real-time industrial applications. However, real-time operational performance was not verified in this study using hardware-specific FPS or latency measurements. The findings reveal that lightweight and optimized deep learning architectures can successfully achieve a balance between performance and complexity in the textile industry.

Keywords

• Fabric defect detection
• Deep learning
• Industrial image processing
• Industry 4.0

Tekstil Üretim Hatlarında Kumaş Hata Tespiti İçin Derin Öğrenme Mimarilerinin Karşılaştırılması ve Yeni Bir Görüntü Veri Seti

Abstract

Tekstil endüstrisinde kalite kontrol süreçleri büyük ölçüde insan denetimine dayanmaktadır ve bu durum yüksek maliyet, düşük hız ve insan kaynaklı hata riski gibi önemli sınırlamalar doğurmaktadır. Özellikle milimetrik boyuttaki kumaş kusurlarının manuel olarak tespit edilmesi zor olup, hatalı ürünlerin üretim hattından geçmesine neden olabilmektedir. Bu çalışmada, tekstil üretim hatlarından elde edilen kumaş görüntülerinin hatalı veya hatasız olarak ayrılmasını amaçlayan, görüntü düzeyinde ikili sınıflandırmaya dayalı otomatik bir kumaş kusur değerlendirme sistemi geliştirilmiştir. Çalışma kapsamında 512×512 çözünürlüğünde 2915 görüntüden oluşan yeni bir veri seti oluşturulmuştur. Veri setinin %20'si bağımsız test kümesi olarak ayrılmış, kalan %80'lik geliştirme kümesinin %10'u doğrulama amacıyla kullanılmıştır. Özgün CNN, VGG16, ResNet50, InceptionV3 ve MobileNetV2 olmak üzere beş farklı derin öğrenme mimarisi karşılaştırmalı olarak analiz edilmiştir. Deneysel sonuçlar, MobileNetV2 modelinin mevcut sabit veri bölünmesinde %99,31 doğruluk, %99,32 kesinlik, %99,31 duyarlılık ve %99,31 F1-skor ile sayısal olarak en yüksek performans değerlerini elde ettiğini göstermiştir. Ayrıca MobileNetV2’nin düşük parametreli ve hesaplama açısından hafif mimarisi, gerçek zamanlı endüstriyel uygulamalar açısından potansiyel bir avantaj sunmaktadır. Bununla birlikte, gerçek zamanlı çalışma performansı bu çalışmada donanıma özgü FPS veya gecikme ölçümleriyle doğrulanmamıştır. Elde edilen bulgular, hafif ve optimize edilmiş derin öğrenme mimarilerinin tekstil sektöründe performans ve karmaşıklık dengesini başarıyla sağlayabileceğini ortaya koymaktadır.

Keywords

• Kumaş kusur tespiti
• Derin öğrenme
• Endüstriyel görüntü işleme
• Endüstri 4.0

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