Endüstriyel Döküm Parçalarda Yüksek Doğruluklu Yüzey Hata Sınıflandırması için Çoklu Özellik Çıkarımı ve Seçimi Temelli Bir Çerçeve

Abstract

Bu çalışma, endüstriyel döküm parçalarının yüzey kusurlarının otomatik olarak tespit edilmesine yönelik görüntü işleme ve makine öğrenmesi tabanlı bir yaklaşım sunmaktadır. Çalışmada Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Scale-Invariant Feature Transform (SIFT), Gray Level Co-occurrence Matrix (GLCM), Gabor filtreleri, renk histogramı, dalgacık (wavelet) dönüşümü, Hu momentleri, Zernike momentleri ve Fourier dönüşümü olmak üzere on farklı öznitelik çıkarım yöntemi kullanılmıştır. Bu yöntemler, 300×300 piksel boyutundaki gri tonlamalı görüntülere beş farklı hücre boyutunda (25×25, 50×50, 100×100, 150×150 ve 300×300) uygulanmıştır. Yüksek boyutluluk sorununu azaltmak amacıyla minimum Redundancy Maximum Relevance (mRMR) ve Ki-kare (χ²) testleri ile öznitelik seçimi gerçekleştirilmiştir. Elde edilen öznitelik kümeleri, MATLAB Classification Learner aracı kullanılarak Fine Tree, Fine KNN, Wide Neural Network, Bagged Trees, Fine Gaussian SVM ve Binary GLM algoritmalarıyla sınıflandırılmıştır. Deneysel sonuçlara göre en yüksek doğruluk oranı %99,7 ile 25×25 hücre boyutunda tüm özniteliklerin kullanıldığı Wide Neural Network modeliyle elde edilmiştir. Bulgular, küçük hücre boyutlarının yüzey ayrıntılarını daha iyi koruyarak sınıflandırma başarımını artırdığını ortaya koymaktadır. Sonuç olarak önerilen yöntem, döküm endüstrisinde yüksek doğruluklu, hızlı ve uygulanabilir bir kalite kontrol sistemi olarak değerlendirilebilecek potansiyele sahiptir.

Keywords

• Döküm kusurları
• Görüntü işleme
• Makine öğrenmesi
• Özellik çıkarımı
• HOG
• LBP

A Multi-Feature Extraction and Selection Framework for High-Accuracy Surface Defect Classification in Industrial Casting Parts

Abstract

This study presents an automated quality inspection approach for detecting surface defects in industrial casting parts using image processing and machine learning techniques. A total of ten different feature extraction methods—Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Scale-Invariant Feature Transform (SIFT), Gray Level Co-occurrence Matrix (GLCM), Gabor filters, color histogram, wavelet transform, Hu moments, Zernike moments, and Fourier transform—were applied to 300×300 grayscale images. To evaluate the effect of spatial resolution, features were extracted at five different cell sizes: 25×25, 50×50, 100×100, 150×150, and 300×300. Dimensionality reduction was performed using minimum Redundancy Maximum Relevance (mRMR) and Chi-square (χ²) feature selection techniques. The resulting feature sets were classified with six different algorithms in MATLAB Classification Learner, including Fine Tree, Fine KNN, Wide Neural Network, Bagged Trees, Fine Gaussian SVM, and Binary GLM. Experimental results demonstrated that the highest accuracy rate of 99.7% was achieved with the Wide Neural Network model trained on the complete feature set at a 25×25 cell size. These findings indicate that smaller cell sizes preserve critical surface details and enhance classification performance. The study highlights that the proposed methodology can serve as a highly accurate, efficient, and practical solution for automated defect detection in the casting industry, offering strong potential for real-world industrial applications.

Keywords

• Casting defects
• Image processing
• Machine learning
• Feature extraction
• HOG
• LBP

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