Yurii Vipshovskyi
@lpnu.ua
Software engineering
Lviv Polytechnic National University
I graduated from Ivan Franko National University of Lviv, where I specialized in computer science, focusing on computer vision and image processing. During my academic journey, I conducted research on various aspects of artificial neural networks and their application in image recognition. One of the highlights of my studies was presenting at a student conference, where I shared my findings on using artificial neural networks for digit detection in images. This experience fueled my passion for exploring innovative solutions in the field of artificial intelligence and computer vision.
As I continue to expand my expertise, I am eager to contribute to the academic and professional communities through impactful research and collaboration.
EDUCATION
2019-2023 Ivan Franko National University of Lviv (Applied Mathematic)
2023-2025 Lviv Polytechnic National University (Software engineering)
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computer Science, Artificial Intelligence
FUTURE PROJECTS
The Detection of the Surface Defects in Materials by Distributed and Invariant Features of Image Intensity
Detecting metal surface defects is a critical step in the quality control process for industrial products. Defects, such as cracks, scratches, cavities, and other irregularities, can significantly affect product performance and durability. Therefore, the automation of this process is becoming increasingly important, especially in the context of high production volumes and growing requirements for product quality. In modern industry, a wide range of methods are used to detect defects, among which the most common are image-based methods. The use of computer vision and image processing allows for a quick and accurate assessment of the surface condition of the material to be inspected, minimizing the human factor and reducing the risk of errors. In this paper, we propose an approach that consists in converting images of metal surfaces into a cumulative reflection for further analysis. The main goal is to detect the presence of defects on the metal surface using a new method of image analys
Applications Invited