Classification of Defects in Welds Using a Convolution Neural Network
| Autor: | Ravil M. Nazarov, Eduard S. Konstantinov, Zinnur M. Gizatullin |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network business.industry Computer science Pattern recognition 02 engineering and technology 01 natural sciences Convolutional neural network Data set 020901 industrial engineering & automation 0103 physical sciences Artificial intelligence business 010301 acoustics |
| Zdroj: | 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). |
| DOI: | 10.1109/elconrus51938.2021.9396301 |
| Popis: | In this paper, the possibility of using a convolutional neural network for analyzing weld defects are considered. A program has been developed for creating a data set for training and classification of weld defects based on the publicly available GDXray database. The VGG-16 model is used for classification. The model and algorithm for training a convolutional neural network are described. Technologies of model retraining control are applied. The main method for solving the problem is to transfer training due to the small number of images for training. The resulting model was applied in a specially created program for detecting and classifying weld defects. Examples of how the program works are presented. This model classifies weld defects into 5 classes with an average accuracy of about 86%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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