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• DISTRIBUTED SYSTEM FOR BARCODE RECOGNITION USING NEURAL NETWORKS

  А.Y. Yurchenko , М.Y. Polenov

  70-79

  2025-10-01

  Abstract ▼

  This work presents a distributed software-hardware system for automated barcode recognition on moving objects in industrial environments. The primary objective of the research is to develop a reliable and adaptive solution capable of consistently reading barcodes regardless of the orientation, speed, or height of objects moving along a conveyor belt. The main focus is not on achieving maximum processing speed, but rather on providing a wide field of view and ensuring reliable recognition of moving objects. Unlike traditional scanners that require precise positioning and expensive hardware, the proposed approach leverages a single network camera and a server equipped with neural processing modules, providing a cost-effective and versatile alternative suitable for a wide range of industrial applications. A key component of the system architecture is a neural image restoration module based on the MPRNet model, which effectively reduces motion blur and optical distortions in video frames. After preprocessing, frames are passed to an object detection module built upon the YOLO architecture, which has been adapted specifically for barcode recognition. Detected barcode data is stored in a database using an ORM interface, enabling seamless integration with existing enterprise systems. To prevent frame loss and maintain high throughput, the system incorporates asynchronous processing mechanisms using multithreading and buffered queues. The relevance of this research stems from the widespread use of barcodes as the primary method of product marking in industrial settings and the increasing demand for automation in product tracking and inventory control. Despite the availability of various vision-based and scanning solutions, most existing systems are not designed to handle unstable or low-quality video streams. The proposed system demonstrates robustness to visual distortions and motion-related artifacts, making it suitable for deployment in real production environments. Its affordability and adaptability also open up possibilities for implementation in logistics, warehousing, and supply chain management.