Computer Vision Applied to Smart Markets: a Case Study for Empty Shelf Detection
Resumo
Computer vision technique is attracting the attention of many industries once it has allowed the deployment of powerful applications capable of detecting patterns and changes in different kinds of environments by using popular hardware and software. Retailing is an example of an industry that has been leveraging the use of this concept in order to create resourceful environments such as smart markets. This paper focuses on demonstrating computer vision’s applicability to detect empty spaces in supermarket shelves using a custom-trained YOLO model. This study provides insights into the practicality of using computer vision and a small single-board computer (SBC) in retail spaces and shows how the results found can be useful to be applied in real-life scenarios.
Palavras-chave:
Retail, Smart Markets, Computer Vision, YOLO
Referências
FYP (2023). Supermarket empty shelf detector computer vision project. [link]. accessed: 2023-10-12.
Ghosh, A., Chakraborty, D., and Law, A. (2018). Artificial intelligence in internet of things. CAAI Transactions on Intelligence Technology, 3(4):208–218.
Gross, R. (2019). How the amazon go store’s ai works. [link]. accessed: 2023-09-24.
Hwangbo, H., Kim, Y. S., and Cha, K. J. (2017). Use of the smart store for persuasive marketing and immersive customer experiences: A case study of korean apparel enterprise. Mobile Information Systems, 2017:4738340.
Kalahiki, C. B. (2020). Computer vision for inventory management. Master’s Thesis - College of Engineering and Science Louisiana Tech University, Ruston, LA.
Sanchez-Ruiz, L., Blanco, B., and Kyguolienė, A. (2018). A theoretical overview of the stockout problem in retail: from causes to consequences. Management of Organizations: Systematic Research, 79(1):103–116.
Savit, A. and Damor, A. (2023). Revolutionizing retail stores with computer vision and edge ai: A novel shelf management system. In 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pages 69–74. IEEE.
Shanmugamani, R. (2018). Deep Learning for Computer Vision. Packt.
Szeliski, R. (2022). Computer vision: algorithms and applications. Springer Nature.
Times, T. N. Y. (2018). Retailers race against amazon to automate stores — the new york times.
Wankhede, K., Wukkadada, B., and Nadar, V. (2018). Just walk-out technology and its challenges: A case of amazon go. 1:254–257.
Ying, X. (2019). An overview of overfitting and its solutions. Journal of Physics: Conference Series, 1168(2):022022.
Ghosh, A., Chakraborty, D., and Law, A. (2018). Artificial intelligence in internet of things. CAAI Transactions on Intelligence Technology, 3(4):208–218.
Gross, R. (2019). How the amazon go store’s ai works. [link]. accessed: 2023-09-24.
Hwangbo, H., Kim, Y. S., and Cha, K. J. (2017). Use of the smart store for persuasive marketing and immersive customer experiences: A case study of korean apparel enterprise. Mobile Information Systems, 2017:4738340.
Kalahiki, C. B. (2020). Computer vision for inventory management. Master’s Thesis - College of Engineering and Science Louisiana Tech University, Ruston, LA.
Sanchez-Ruiz, L., Blanco, B., and Kyguolienė, A. (2018). A theoretical overview of the stockout problem in retail: from causes to consequences. Management of Organizations: Systematic Research, 79(1):103–116.
Savit, A. and Damor, A. (2023). Revolutionizing retail stores with computer vision and edge ai: A novel shelf management system. In 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pages 69–74. IEEE.
Shanmugamani, R. (2018). Deep Learning for Computer Vision. Packt.
Szeliski, R. (2022). Computer vision: algorithms and applications. Springer Nature.
Times, T. N. Y. (2018). Retailers race against amazon to automate stores — the new york times.
Wankhede, K., Wukkadada, B., and Nadar, V. (2018). Just walk-out technology and its challenges: A case of amazon go. 1:254–257.
Ying, X. (2019). An overview of overfitting and its solutions. Journal of Physics: Conference Series, 1168(2):022022.
Publicado
17/11/2024
Como Citar
MESQUITA, Renzo P.; TEIXEIRA, Eduardo H.; RIBEIRO, Avner J. G.; GANDOLPHO, Bernardo D.; PONTES, Fábio Luiz F..
Computer Vision Applied to Smart Markets: a Case Study for Empty Shelf Detection. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 460-471.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.245034.