Abstract
Currently, the development of computer vision makes it possible to solve many problems of detecting defects at various industrial facilities. One of the promising areas of application of these technologies is to identify inconsistencies in geometric parameters on cylindrical products. The purpose of this work is to review and systematize modern computer vision methods used to solve the problem of detecting defects on vertical cylindrical surfaces. The study analyzed existing approaches to the extraction of spatial characteristics of objects, including methods of stereo vision, spatial filtering and 3D reconstruction. Algorithms for identifying the main landmarks on a cylindrical surface were considered, which makes it possible to bind the coordinate system and localize areas of possible defects. Methods for estimating geometric deviations on the surface, which can act as criteria for detecting defects, have also been studied. As a result of the analysis, a classification of computer vision methods applicable to the problem of detecting defects on cylindrical objects was proposed. Promising directions for further research in the field of improving the accuracy of defect detection through a combination of various image processing algorithms have been identified.
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