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Methods Of Identification Of Objects On Industrial Lines
(International Journal of Academic Engineering Research (IJAER), 2024) Yechevskyi, A.; Sotnik, S.
This paper examines modern methods of object identification in industrial production lines, focusing on quality control automation and production efficiency optimization. The research analyzes both classical computer vision approaches (including edge detection, contour analysis, Hough transformation, and template matching) and statistical methods for object identification and quality assessment. The study provides comprehensive comparison of various algorithms' effectiveness based on speed, noise tolerance, and accuracy criteria. Special attention is given to evaluating these methods' performance under different production conditions, considering factors such as lighting, surface characteristics, background environment, geometric parameters, and movement speed. Through systematic analysis of existing identification technologies, research establishes key implementation factors for industrial vision systems and provides structured recommendations for deploying identification systems in manufacturing environments. The paper offers valuable insights for industrial enterprises seeking to enhance their quality control processes through modern computer vision and object identification systems.