2023 №02 (04) DOI of Article
2023 №02 (06)

Technical Diagnostics and Non-Destructive Testing 2023 #02
Technical Diagnostics and Non-Destructive Testing #2, 2023, pp. 34-40

Automation of the process of segmentation of images of metal surface defects using the neural network U-Net

Y.V. Steshenko, A.S. Momot, A.G. Protasov O.V. Muraviov

National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute». 37 Beresteysky Avenue, 03056, Kyiv, Ukraine. E-mail: yaroslav.steshenko@ukr.net; drewmomot@gmail.com

The paper deals with the task of automated segmentation of images of defects metal surfaces. The aim of the study is to improve segmentation algorithms using deep learning methods. The expediency of using the U-Net neural network, which is effective in the tasks of semantic image segmentation, is substantiated. With the help of a special architecture, the network can create segmentation masks with high efficiency. The training dataset for the neural network contained images of four classes of defects, including chips, cracks, and stains. As a result of analyzing the distribution of defect classes in the training dataset, it was concluded that the classes were unbalanced, which negatively affects the training results. To evaluate the quality of network training, a set of metrics such as Accuracy, F1 Score, and IOUScore is considered. The feasibility of using these metrics is analyzed, taking into account the features of the training data set. It is proved that under conditions of significant imbalance of classes, the Accuracy metric does not reflect the real quality of the model. The influence of different variants of the ResNet architecture backbone on the training results is analyzed. It is determined that the best results are shown by the ResNet18 model, which managed to obtain a Dice coefficient of 69 % and an IOUScore of 53 % on the test data set. It is proved that an increase in the number of model parameters does not always lead to an improvement in the reliability of the results. The article provides examples of test images and defect masks and countures predicted by the neural network. 13 Ref., 1 Tabl., 7 Fig.
Keywords: metal surfaces, image segmentation, neural networks

Received: 12.04.2023


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