DISTRIBUTED SYSTEM FOR BARCODE RECOGNITION USING NEURAL NETWORKS
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.
References
1. Arslanova L.S. Ispol'zovanie shtrikhovogo kodirovaniya [Use of barcode coding], Ekonomika i sotsium [Economics and Society], 2016, No. 2 (21), pp. 42-44.
2. Tan M., Pang R., Le Q.V. EfficientDet: Scalable and Efficient Object Detection, arXiv, 2020. arXiv:1911.09070v7 [cs.CV].
3. Buren' M.N. Obzor vozmozhnostey YOLO v8 v ramkakh resheniya zadachi raspoznavaniya izobra-zheniy [Review of YOLO v8 capabilities for image recognition tasks], Komp'yuternye sistemy i seti: Sb. statey 60-y nauchnoy konferentsii aspirantov, magistrantov i studentov, BGUIR [60th Anniversary Sci-entific Conference of Postgraduates, Master's Students and Students of BSUIR]. Minsk: BGUIR, 2024, pp. 28-33.
4. Hansen D.K., Nasrollahi K., Rasmussen C.B., Moeslund T.B. Real-Time Barcode Detection and Classi-fication Using Deep Learning, Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017). Vol. 1. SciTePress, 2017, pp. 321-327. DOI: 10.5220/0006508203210327.
5. Abuolaim A. et al. NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel Images: Methods and Results, CVPRW, 2021, pp. 578-587. DOI: 10.1109/CVPRW53098.2021.00070.
6. Nah S. et al. NTIRE 2021 Challenge on Image Deblurring, CVPRW, 2021, pp. 149-165. DOI: 10.1109/CVPRW53098.2021.00025.
7. Ren S., He K., Girshick R., Sun J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv, 2016. arXiv:1506.01497v3 [cs.CV].
8. Liang J., Cao J., Sun G., Zhang K., Van Gool L., Timofte R. SwinIR: Image Restoration Using Swin Transformer, arXiv, 2021. arXiv:2108.10257v1 [eess.IV].
9. Strel'tsov D.S., Butenko E.A. Vlan id enumeration. Metody zashchity [VLAN ID enumeration: security methods], Mirovaya nauka [World Science], 2024, No. 12 (93), pp. 120-133.
10. Neustroev A.V. Sokhranit' JSON dannye v bazu dannykh sqlalchemy [Saving JSON data to a database with SQLAlchemy], Problemy nauki [Problems of Science], 2016, No. 12 (13).
11. Zhao T., Ma X., Li X., Zhang C. MPR-Net: Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting, Proceedings of the AAAI Conference on Artificial Intelligence, 2023. arXiv:2307.06736.
12. Chen X., Li Z., Pu Y., Liu Y., Zhou J., Qiao Y., Dong Ch. A Comparative Study of Image Restoration Networks for General Backbone Network Design, Proceedings of the IEEE/CVF Conference on Com-puter Vision and Pattern Recognition (CVPR), 2024, pp. 74-91.
13. Bueno G.G., Suares O.D., Espinosa A. i dr. Obrabotka izobrazheniy s pomoshch'yu OpenCV [Image Processing with OpenCV]: transl. from engl. Slinkin A.A. Moscow: DMK Press, 2016, 210 p.
14. Boracchi G., Foi A. Modeling the Performance of Image Restoration from Motion Blur, IEEE Transac-tions on Image Processing, 2012, Vol. 21, Iss. 8. – P.3502-3517. DOI: 10.1109/TIP.2012.2192126.
15. Verbitskaya V.I., Korshikova D.V., Kupchina E.V. Realizatsiya algoritma vosstanovleniya «Fil'tr Vinera» na Python [Implementation of the Wiener Filter image restoration algorithm in Python], Informatsionnye tekhnologii i upravlenie: Mater. 60-oy nauchnoy konferentsii aspirantov, magistrantov i studentov, 2024 [Information Technologies and Control: Proceedings of the 60th Scientific Conference of Postgraduates, Master's Students and Students, 2024]. Minsk: BGUIR, 2024, pp. 69.
16. Ivashchenko V.Yu. Vosstanovlenie izobrazheniy i razlichnye tekhniki restavratsii ispol'zuemye v sfere obrabotki vizual'nykh tsifrovykh resursov [Image restoration and restoration techniques used in visual digital resource processing], Teoriya i praktika sovremennoy nauki [Theory and Practice of Modern Sci-ence], 2019, No. 12 (54), pp. 143-147.
17. Korshikova D.V., Kukin D.P., Kupchina E.V. Algoritmy vosstanovleniya izobrazheniy [Image restora-tion algorithms]. Informatsionnye tekhnologii i sistemy 2023 (ITS 2023): Mater. Mezhdunarodnoy nauchnoy konferentsii [Information Technologies and Systems 2023 (ITS 2023): Proceedings of the In-ternational Scientific Conference]. Minsk: BGUIR, 2023, pp. 149-150.
18. Abakarov G.M. Analiz i otsenka mnogopotochnosti dlya effektivnosti prilozheniy [Analysis and Evalua-tion of Multithreading for Application Efficiency], Vestnik nauki [Science Bulletin], 2023,
No. 12 (69), pp. 1094-1098.
19. Kadomskiy A.A., Zakharov V.A. Effektivnost' mnogopotochnykh prilozheniy [Efficiency of Multithread-ed Applications], Nauchnyy zhurnal [Scientific Journal], 2016, No. 7 (8).
20. Zlobina N.V., Volzhankin N.V., Posobilov N.E. Obespechenie tsentralizovannogo monitoringa dlya sis-tem slozhnoy arkhitektury s bol'shim ob"emom dannykh [Ensuring centralized monitoring for complex architecture systems with large data volumes], Tr. NGTU im. R.E. Alekseeva [Proceedings of NSTU nam. R.E. Alekseev], 2017, No. 4 (119), pp. 18-23.
