Criteria analysis of radiation nondestructive testing data processing models

V.D. Korchagin, V.S. Kuvshinnikov, E.E. Kovshov

Abstract


A research of neural network models for the task of radiation nondestructive testing data processing in the context of production defect detection is done. The analysis is based on the results of the author's previous research of actual SOTA-architectures used for image classification and object detection tasks. The study considers the performance of the following neural network models: ResNet, EfficientNet, VGGNet, MobileNet and ViT. The analysis was based on multiple measurements of the time characteristics of both individual image instances and passing the full dataset, as well as the speed and accuracy of training depending on the size of the training sample and the complexity of the base model. The training process utilized a learning architecture method without the participation of pre-trained weights. A dataset including both labeled and unlabeled data on defects in metal of various types was compiled from several public sources.

Results are summarized that the use of images as an input tensor is not effective enough to achieve optimal accuracy of the results for the task at hand. In this regard, further investigation of models capable of taking into account additional meta-information is required. The obtained results are of practical importance for designing the neural network architecture for solving the problem of completing the algorithms for image retrieval based on the results of radiation testing in industry.

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References


DOI: 10.25559/INJOIT.2307-8162.12.202404.23-31

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