Denim Kumaşından Otomatik Yüksek Çözünürlüklü Bıyık Desen Sentezi

Year 2022, Volume: IDAP-2022 : International Artificial Intelligence and Data Processing Symposium , 86 - 100, 10.10.2022

Emrullah Şahin , Muhammed Fatih Talu

https://doi.org/10.53070/bbd.1173565

Abstract

Denim kumaşlarındaki bıyık desenlerinin elde edilmesi uzman bir kişi tarafından manuel olarak yaklaşık 2-3 saat sürmektedir. Zamansal maliyetle birlikte kişi bazlı üretimden kaynaklı hatalar meydana gelmektedir. Bu problemin çözümü için bu çalışmada güncel Çekişmeli üretici ağlardan biri olan Pix2PixHD mimarisi kullanılmıştır. Derin öğrenme tabanlı bu mimarinin kullanımı için 2048x1024 ebatlarındaki 589 adet Denim kumaşı-Bıyık Deseni görüntü çiftinden oluşan Denim2BıyıkHD veri kümesi hazırlanmıştır. Mimarinin en uygun sonuçları verebilmesi için üzerinde iyileştirmeler yapılarak geliştirilmiş versiyonu önerilmiştir. Eğitim işleminden sonra geliştirilmiş yöntemle birlikte görüntü kalitesinde yaklaşık 92% oranında başarım sağlanırken, zamansal üretim işlem maliyeti 1 saniyenin altına düşürülmüştür. Bu çalışmayla birlikte Denim kumaşlarındaki bıyık desenlerinin otomatik, yüksek kalitede, hızlı ve nesnel bir şekilde üretimini sağlayan yazılımsal bir sistem geliştirilmiştir.

Keywords

Denim kumaş, Bıyık deseni, Görüntü Sentezi, Çekişmeli üretici ağlar

Supporting Institution

İnönü Üniversitesi Bilimsel Araştırma ve Koordinasyon Birimi

Project Number

FKP-2021-2144

Thanks

Bu çalışma Baykan Denim A.Ş. ve İnönü Üniversitesi Bilimsel Araştırma Projeleri Birimi (BAP) tarafından “FKP-2021-2144” proje numarasıyla desteklenmektedir. Baykan Denim A.Ş. ve İnönü Üniversitesine bu destekleri için teşekkür ederiz.

Automatic High-Resolution Mustache Pattern Synthesis From Denim Fabric

Abstract

Obtaining the mustache patterns on denim fabrics takes about 2-3 hours manually by an expert. Along with the temporal cost, errors occur due to person-based production. To solve this problem, Pix2PixHD architecture, one of the current Generative adversarial networks, is used in this study. For the use of this deep learning-based architecture, Denim2BıyıkHD dataset consisting of 589 Denim fabric-Mustache Pattern image pairs in 2048x1024 dimensions was prepared. For the architecture to give the most appropriate results, its improved version has been proposed. With the improved method after the training process, approximately 92% of the image quality was achieved, while the temporal production process cost was reduced to less than 1 second. With this study, a software system was developed that enables the automatic, high quality, fast and objective production of mustache patterns on denim fabrics.

Keywords

Denim fabric, Mustache pattern, Image synthesis, Generative adversarial networks

Project Number

FKP-2021-2144

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