Evrişimli otokodlayıcı tabanlı hata tespiti ile desteklenen otomatik kumaş inceleme sistemi geliştirilmesi

Year 2024, Volume: 13 Issue: 4, 1100 - 1114, 15.10.2024

Muharrem Mercimek , Muhammed Ali Öz , Özgür Turay Kaymakçı

https://doi.org/10.28948/ngumuh.1481769

Abstract

Endüstriyel otomatik kumaş inceleme sistemi, endüstride klasik inceleme tekniklerine göre hem toplam üretim miktarını hem de kaliteyi artıran kritik bir teknolojidir. Bu çalışma, kumaşlar için güvenilir ve etkili bir gerçek zamanlı otomatik görsel inceleme sistemi oluşturmayı amaçlamaktadır ve odak noktası olarak hata tespiti üzerinde yoğunlaşmaktadır. Çalışmanın hedefleri; hızlı bir şekilde görüntüleri yakalama ve işleme yeteneğine sahip gelişmiş teknolojiye sahip bir sistem kurmak, kullanımdaki kumaşları otomatik olarak öğrenme ve tarayabilme yeteneğine sahip bir sistem geliştirme ve, doğru kumaş hata tespiti ve sınıflandırması için akıllı bir yaklaşım oluşturmak şeklinde ifade edilebilir. Çalışmada, evrişimli otokodlayıcı modeli kullanarak denetimsiz bir kumaş hata tespiti ve evrişimli sinir ağı modeli kullanarak hata sınıflandırma geliştirme işlemleri üzerinde durulmaktadır. Sınıflandırmada kullanılan evrişimli sinir ağının girişine evrişimli otokodlayıcı tarafından üretilen özellik vektörü sunulmaktadır. Deney sonuçları analiz edildiğinde, hem hataları tespitte hem de bunları sınıflandırmada önemli başarı sergilenmiştir ve yaklaşımın gerçek zamanlı görsel inceleme sistemlerindeki etkinliğini gösterilmiştir.

Keywords

Kumaş hatası tespiti, Kumaş inceleme sistemi, Evrişimli otokodlayıcı

Supporting Institution

This research is funded by The Scientific and Technological Research Council of Turkey (TUBITAK) under project number 118E607

Automated fabric inspection system development aided with convolutional autoencoder-based defect detection

Abstract

Industrial automatic fabric inspection system, a critical technology in the industry, enhances both total production quantity and quality compared to conventional inspection techniques. This study aims to create a reliable and effective real-time automated visual inspection system for fabrics, focusing on defect detection. The goals of the study can be stated as; installing a system with advanced technology for capturing and processing images swiftly, the development and deployment of a system capable of autonomously learning and scanning fabrics in use, and the creation of a smart framework for accurate fabric defect detection and classification. We focus on the development of unsupervised fabric defect detection using a convolutional autoencoder model, and defect classification using a convolutional neural network model, which takes input as the feature vector generated by the convolutional autoencoder. The experimental outcomes have displayed significant success rates in both detecting defects and classifying them, confirming the effectiveness of the framework in real-time visual inspection systems.

Keywords

Fabric defect detection, Fabric inspection system, Convolutional autoencoder

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