Ahşap Kusur Tespiti İçin Optimize Edilmiş AlexNet Mimarisi

Year 2023, Volume: 2 Issue: 2, 20 - 28, 29.12.2023

Kenan Kılıç , Uğur Özcan

Abstract

Bu makale, AlexNet mimarisi kullanılarak kusurlu ve kusursuz ahşap yüzey görüntülerinin sınıflandırılması üzerine odaklanmaktadır. İlk olarak, karışık olan yüzey görüntüleri kusurlu ve kusursuz diye ikiye ayrılmış ve yeniden düzenlenmiştir. Bu veri kümesinde 1992 kusursuz, 18 284 kusurlu ahşap yüzey görüntüsü bulunmaktadır. Ahşap yüzey görüntüleri üzerinde toplam 43 000 ahşap kusur bulunmaktadır. AlexNet mimarisi transfer öğrenme yaklaşımı kullanılarak deneyler gerçekleştirilmiştir. Deneylerde, farklı epoch sayıları (25 epoch, 50 epoch) ve veri artırma yöntemi kullanılarak AlexNet modelin eğitimi gerçekleştirilmiş ve sonra test edilmiştir. Ahşap yüzey kusur tespitinde ikili sınıflamada sonuç olarak, AlexNet mimarisi ile kusurlu ve kusursuz ahşap yüzey görüntülerinin sınıflandırılması sonucunda en başarılı sonuçları AlexNet Augmented* modelinin 50 epoch sonrasında elde ettiği görülmektedir. Bu modelde, doğruluk değeri 0.9687, AUC değeri 0.9892 olarak hesaplanmıştır. Yaklaşık %97 oranında ahşap kusur tespiti sonucu bu çalışmada elde edilmiştir. Ayrıca, hassasiyet, geri çağırma ve F-skor değerleri de 0.97 olarak belirlenmiştir. Bu sonuçlar, ahşap yüzey kusur tespitinde AlexNet modelinin yüksek bir performans sergilediğini göstermektedir.

Keywords

Ahşap kusur, Bilgisayarlı görü, Yapay zeka, Derin öğrenme, Transfer öğrenme

AlexNet Architecture Optimized for Wood Defect Detection

Abstract

This paper focuses on the classification of imperfect and perfect wood surface images using AlexNet architecture. Firstly, the mixed surface images are divided into imperfect and perfect and reorganised. This dataset contains 1992 undefective and 18 284 defective wood surface images. There are a total of 43 000 wood defects on this dataset. Experiments are carried out using the AlexNet architecture transfer learning approach. In the experiments, the AlexNet model is trained using different epoch numbers (25 epochs, 50 epochs) and data augmentation method. It is then tested. As a result of binary classification in wood surface defect detection, it is seen that the AlexNet Augmented* model obtained the most successful results after 50 epochs as a result of the classification of defective and perfect wood surface images with AlexNet architecture. In this model, the accuracy rate is calculated as 0.9687 and AUC value as 0.9892. Approximately 97% of wood defect detection results are obtained in this study. In addition, the precision, recall and F-score values are determined as 0.97. These results show that the AlexNet model has a high performance in wood surface defect detection.

Keywords

Wood defect, Computer vision, Artifical intelligence, Deep learning, Transfer learning

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