Görüntü İşleme ve Gözetimli Öğrenme Yardımıyla Endüstriyel Beyaz Kuvars Taş Sınıflandırması

Year 2022, Volume: 9 Issue: 2, 801 - 813, 31.05.2022

Fatih Akkoyun , Orçun Ekin , Özel Sebetci

https://doi.org/10.31202/ecjse.1010036

Abstract

Endüstriyel maden ve taş sınıflandırma uygulamalarına yönelik, görü tabanlı bir sınıflandırma metodu geliştirilmiştir. Görü tabanlı çözümleme taşlara ait görsel parametrelerin çıkartılması için kullanılmış ve makine öğrenme algoritmalarından yararlanılarak renk ve şekil parametrelerine göre sınıflandırılmıştır. Gerçekleştirilen deneylerde, her biri gelişigüzel seçilmiş 10 adet taş içeren 4 grup, toplamda 40 karışık renk ve şekil özellikleri gösteren numune sınıflandırılmıştır. Her bir numunenin 4 farklı açıdan alınan birer görüntüsü, görsel parametrelerin elde edilmesi amacıyla işlenmiştir. Elde edilen verinin %67’si programın eğitimi amacıyla; kalan veri ise test süreçlerinde kullanılmıştır. Geliştirilen yöntem, durgun görüntüleri çekilerek etiketlenen maden numunelerinin %98’e kadar sınıflandırılmasını sağlamaktadır. Yöntemin verimliliğinin daha açık bir şekilde görülmesi ve sonuçların daha iyi değerlendirilmesi amacıyla deneysel verilerden türetilmiş bir karışıklık matrisi kullanılmıştır.

Keywords

Bilgisayarlı Görü, Taş Sınıflandırma, Görüntü İşleme, En Yakın Komşu

Industrial White Quartz Stone Classification Using Image Processing and Supervised Learning

Abstract

A vision-based stone classifying method was developed for industrial mine stone grading applications. The image-based solution is used to extract visual parameters and stones are classified by their color and shape parameters with the help of the machine learning algorithms. In the experiments, four groups, each including ten arbitrarily selected stones; in total forty stone samples with complex colors and shapes were examined. Four different images are captured under four different angles and processed to extract visual parameters of each stone sample. In training stage 67% of the data were used for training and rest were used for testing process. The method correctly classifies mine stones up to 98% from still images using labeled inputs. A confusion matrix derived from the experimental results is employed in order to emphasize the efficiency of the system more clearly and emphasize the results in a certain manner.

Keywords

Computer vision, stone grading, image processing, nearest neighbors.

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