Akademik Veri Yönetim Sistemi
Bozok Journal of Engineering and Architecture, cilt.2, sa.2, ss.20-28, 2023 (Hakemli Dergi)
Wood defect detection plays an important role in production and quality planning in woodworking industrial engineering. This paper focuses on the classification of imperfect and perfect wood surface images using AlexNet architecture. First, the mixed surface images are divided into defective and perfect images and reorganized. The dataset used in this study 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.