Digital Technologies for Automation of Warehouse Accounting of Assembly Components

Using machine vision technology to manage assembly kits in a warehouse allows for automated inventory control and management, improving the accuracy and speed of data processing. The implementation of such systems reduces the influence of the human factor, minimizes errors during inventory, and ensures prompt updating of information in the accounting system. This article discusses methods for recognizing objects using computer vision, including image processing, marker or barcode detection, and the use of neural network algorithms for component classification. The advantages and limitations of different approaches are analyzed. The results of this study demonstrate the effectiveness of using machine vision for automated inventory control of assembly kits, which helps optimize logistics processes and reduce warehousing costs.

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Publication Details

Journal
Journal of Machinery Manufacture and Reliability
Published
2025-12-01
DOI
https://doi.org/10.1134/s105261882570133x
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Digital Technologies for Automation of Warehouse Accounting of Assembly Components

A. A. Antonova, E. V. Kopytova, N. V. Vodolazskaya
Journal of Machinery Manufacture and Reliability
Industrial Vision Systems and Defect Detection
article

Digital Technologies for Automation of Warehouse Accounting of Assembly Components

A. A. Antonova, E. V. Kopytova, N. V. Vodolazskaya
article en

Abstract

Using machine vision technology to manage assembly kits in a warehouse allows for automated inventory control and management, improving the accuracy and speed of data processing. The implementation of such systems reduces the influence of the human factor, minimizes errors during inventory, and ensures prompt updating of information in the accounting system. This article discusses methods for recognizing objects using computer vision, including image processing, marker or barcode detection, and the use of neural network algorithms for component classification. The advantages and limitations of different approaches are analyzed. The results of this study demonstrate the effectiveness of using machine vision for automated inventory control of assembly kits, which helps optimize logistics processes and reduce warehousing costs.

Journal of Machinery Manufacture and ReliabilityVol. 54(S2)
MIREA - Russian Technological University (RU)
Openalex Percentile: Top 31%
Industrial Vision Systems and Defect Detection
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