Classification of Steel Surface Defects Using Bilinear CNN

Year 2024, Volume: 36 Issue: 1, 267 - 280, 28.03.2024

Emre Güçlü İlhan Aydın Erhan Akın

https://doi.org/10.35234/fumbd.1323339

Abstract

Steel is one of the most widely used building materials in the industry. With the increasing competition among steel manufacturers, the surface quality of steel products has become more important. Defects that may occur on steel surfaces can cause bigger problems when they are not detected. Today, steel surface defect detection systems have replaced traditional defect detection methods. Surface imperfections have an anomalous appearance as opposed to the appearance of solid steel. Using deep learning-based methods to detect these defects has many advantages over expensive methods. Therefore, with Industry 4.0, computer vision-based methods are more widely used for the detection of defects that may occur on steel products. In this study, bilinear convolutional neural network (Bilinear-CNN) is used to classify defects that may occur on steel surfaces. In the dataset used for training, defect and non-defect data are very similar to each other. The bilinear pooling method is capable of extracting higher order and spatially unordered information. Thus, it has been shown to achieve high performance in similar datasets. The performance of the proposed method has been evaluated for different networks. Bilinear Xception model obtained the highest result with an accuracy rate of 98.26%. The results show that the bilinear convolutional neural network achieves high performance in classifying datasets consisting of similar images.

Keywords

Steel surface defects, Defect detection, Bilinear CNN

Project Number

5210082

Çift Doğrusal CNN Kullanarak Çelik Yüzey Kusurlarının Sınıflandırılması

Abstract

Çelik, endüstride oldukça fazla kullanılan yapı malzemelerinden biridir. Çelik üreticileri arasındaki rekabetin artmasıyla birlikte çelik ürünlerin yüzey kalitesi daha önemli bir hale gelmiştir. Çelik yüzeylerde oluşabilecek kusurlar tespit edilemediğinde daha büyük sorunlara neden olabilmektedir. Günümüzde, çelik yüzey kusurlarını algılama sistemleri, geleneksel kusur tespit yöntemlerinin yerini almıştır. Yüzey kusurları, sağlam çelik görünümünden farklı olarak anormal görünüme sahiptir. Bu kusurların tespiti için derin öğrenme tabanlı yöntemlerin kullanılması, pahalı yöntemlere göre birçok avantaja sahiptir. Bu nedenle, Endüstri 4.0 ile birlikte çelik ürünler üzerinde oluşabilecek kusurların tespiti için bilgisayarlı görmeye dayalı yöntemler daha yaygın olarak kullanılmaktadır. Bu çalışmada, çelik yüzeylerde oluşabilecek kusurların sınıflandırılması için çift doğrusal evrişim sinir ağı (Bilinear-CNN) kullanılmıştır. Eğitim için kullanılan veri kümesinde kusurlu ve kusursuz veriler birbirine oldukça benzerdir. Çift doğrusal havuzlama yöntemi, daha yüksek dereceli ve uzamsal sırasız bilgileri çıkarabilme yeteneğine sahiptir. Böylece benzer veri kümelerinde yüksek performans elde ettiği gösterilmiştir. Önerilen yöntemin performansı farklı ağlar için değerlendirilmiştir. %98,26 doğruluk oranıyla en yüksek sonucu Bilinear Xception modeli elde etmiştir. Sonuçlar, çift doğrusal evrişimli sinir ağının benzer görüntülerden oluşan veri kümelerini sınıflandırmada yüksek performans elde ettiğini göstermektedir.

Keywords

Çelik yüzey kusurları, Kusur tespiti, Çift doğrusal CNN

Supporting Institution

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TUBITAK)

Project Number

5210082

Thanks

Bu çalışma, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TUBITAK) tarafından 5210082 numaralı proje ile desteklenmiştir.

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