Synthesis of virtual reality and computer vision technologies in industrial radiography simulator

Purpose of research . Nowadays, the digitalization of production is considered as the most important aspect of technological growth to improve the competitiveness of enterprises. An innovative approach combining virtual reality and computer vision technologies into a single tool designed to improve the quality of practice-oriented training in the field of industrial radiography is proposed. Within the framework of the article the research of the most effective models of artificial neural networks in application to the task of detection of defective areas of welded metal joints on radiographic images is carried out. A detailed analysis of the YOLOv8 architecture with respect to the detection of small-sized defects is carried out. A method for synthesizing virtual reality and computer vision technologies in a single educational tool for industrial radiography is described. Methods . Methods of empirical research, system analysis and synthesis of related information technologies were used in this work. Results . The empirical study revealed the limited effectiveness of the YOLOv10 model as applied to the generalization of features of objects of small dimensionality and low contrast. YOLOv8 showed more practical results and greater stability when generalizing the contour component of defects. In the process of system analysis of YOLOv8 architecture the loss of spatial information when using sequential convolutional operations preceding upsampling was revealed. Modification of the basic YOLOv8 architecture was performed in order to improve the generalization ability of lowdimensional and low-contrast defects. The methodology of synthesis of virtual reality and computer vision technologies in the form of an intelligent assistant for intellectualization of nondestructive testing process is presented. Conclusion . The integration of the above synthesis method into a single software product will improve the quality of specialist training and open access to innovative methods of improving professional skills at every stage of a professional career.

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Publication Details

Journal
Proceedings of the Southwest State University Series IT Management Computer Science Computer Engineering Medical Equipment Engineering
Published
2025-02-24
DOI
https://doi.org/10.21869/2223-1536-2024-14-4-98-115
Primary Topic
Technology and Human Factors in Education and Health
Type
article
Field-Weighted Citation Impact
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article

Synthesis of virtual reality and computer vision technologies in industrial radiography simulator

V. S. Kuvshinnikov, Е. Е. Ковшов, V. D. Korchagin
Proceedings of the Southwest State University Series IT Management Computer Science Computer Engineering Medical Equipment Engineering
Technology and Human Factors in Education and Health
article

Synthesis of virtual reality and computer vision technologies in industrial radiography simulator

V. S. Kuvshinnikov, Е. Е. Ковшов, V. D. Korchagin
article en

Abstract

Purpose of research . Nowadays, the digitalization of production is considered as the most important aspect of technological growth to improve the competitiveness of enterprises. An innovative approach combining virtual reality and computer vision technologies into a single tool designed to improve the quality of practice-oriented training in the field of industrial radiography is proposed. Within the framework of the article the research of the most effective models of artificial neural networks in application to the task of detection of defective areas of welded metal joints on radiographic images is carried out. A detailed analysis of the YOLOv8 architecture with respect to the detection of small-sized defects is carried out. A method for synthesizing virtual reality and computer vision technologies in a single educational tool for industrial radiography is described. Methods . Methods of empirical research, system analysis and synthesis of related information technologies were used in this work. Results . The empirical study revealed the limited effectiveness of the YOLOv10 model as applied to the generalization of features of objects of small dimensionality and low contrast. YOLOv8 showed more practical results and greater stability when generalizing the contour component of defects. In the process of system analysis of YOLOv8 architecture the loss of spatial information when using sequential convolutional operations preceding upsampling was revealed. Modification of the basic YOLOv8 architecture was performed in order to improve the generalization ability of lowdimensional and low-contrast defects. The methodology of synthesis of virtual reality and computer vision technologies in the form of an intelligent assistant for intellectualization of nondestructive testing process is presented. Conclusion . The integration of the above synthesis method into a single software product will improve the quality of specialist training and open access to innovative methods of improving professional skills at every stage of a professional career.

Proceedings of the Southwest State University Series IT Management Computer Science Computer Engineering Medical Equipment EngineeringVol. 14(4)
D. Mendeleyev University of Chemical Technology of Russia (RU), Qingdao Installation & Construction (China) (CN)
Openalex Percentile: Top 95%
Technology and Human Factors in Education and Health
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