@lpnu.ua
Software engineering
Lviv Polytechnic National University
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