Vision Based Detection Using SSD Model For Beverage Manufacturing Industry

  • Unique Paper ID: 164264
  • PageNo: 3097-3104
  • Abstract:
  • This project focuses on the development of a SSD model for the automatic detection of beverage bottles in an industrial setting. Beverage bottles come in various variations, and the goal was to create a robust system capable of identifying different classes of these bottles. The project includes data collection, annotation, augmentation, model training, and deployment on edge devices.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{164264,
        author = {Gayathri Devi Malisetty and Sai Joshitha and Lakshmi Nikitha and Mounika },
        title = {Vision Based Detection Using SSD Model For Beverage Manufacturing Industry},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {10},
        number = {12},
        pages = {3097-3104},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164264},
        abstract = {This project focuses on the development of a SSD model for the automatic detection of beverage bottles in an industrial setting. Beverage bottles come in various variations, and the goal was to create a robust system capable of identifying different classes of these bottles. The project includes data collection, annotation, augmentation, model training, and deployment on edge devices.},
        keywords = {Data Augmentation, Annotation, SSD Model, Roboflow.},
        month = {June},
        }

Cite This Article

Malisetty, G. D., & Joshitha, S., & Nikitha, L., & Mounika, (2024). Vision Based Detection Using SSD Model For Beverage Manufacturing Industry. International Journal of Innovative Research in Technology (IJIRT), 10(12), 3097–3104.

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