(50-5) 16 * << * >> * Russian * English * Content * All Issues

Multi-Class Object Classification for Smart Retail Store Management Using “You Only Look Once” Model
X. Bai1, S.Z. Jiang2

1 Department of Artificial intelligence, Guangxi Polytechnic University, Nanning, 530226, China;
2 Department of Intelligent Manufacturing, Guangxi Polytechnic University, Nanning, 530226, China

  Full text (PDF)

DOI: 10.18287/COJ1713

Article ID: 1713

Language: English

Abstract:
Smart Retail Management relies heavily on multi-class item classification to automate inventory management and security. While deep learning techniques have shown promise, many methods struggle to achieve both high accuracy and real-time processing speeds. This paper evaluates the performance of the state-of-the-art You Only Look Once v8 network for the specific task of multi-class jewelry classification in a retail setting. To facilitate this, we developed a custom dataset of jewelry items. The You Only Look Once v8 architecture was trained on this dataset, and its performance was rigorously validated through extensive testing. Our results demonstrate that the standard You Only Look Once v8 model can deliver highly accurate and real-time classification, meeting the demanding requirements of smart retail management and establishing its viability for practical deployment.

Keywords:
multi-class object classification, deep learning, smart retail management, YOLOv8, computer vision

Acknowledgements:
This investigation was backed by the project of Research and Practice of Intelligent Poultry Farming Monitoring System Based on Internet of Things Technology. (No.2023KY1026)

Citation:
Bai X, Jiang SZ. Multi-Class Object Classification for Smart Retail Store Management Using “You Only Look Once” Model. Computer Optics 2026; 50 (5): 1713. doi: 10.18287/COJ1713.

References:

  1. Vadruccio R, Seghezzi A, Tumino A. Smart technologies for retailing: Who, what, where and why. International Journal of Retail & Distribution Management 2024; 52(13): 79-102.
  2. Liu B. A deep learning-based object representation algorithm for smart retail management. Journal of The Institution of Engineers (India): Series B 2024; 105(5): 1121-1128.
  3. Bhatt AP, Nahar P. Smart retail inventory management using AI-driven IoT. Journal of Supply Chain Management Systems 2025; 14(1).
  4. Sivalakshmi P, Kavitha U, Usha R, Pattanaik O, Maniraj SP, Srinivasan C. Smart retail store surveillance and security with cloud-powered video analytics and transfer learning algorithms. In: Proceedings of the Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE; 2024: 242-247.
  5. Ghazali FF, Karim RA, Wahid SDM, Naseri RNN. Smart retail, smarter decisions: Conceptualizing the antecedents of consumer intention to use intelligent retail systems. International Journal of Art and Design 2025; 9(2/SI-3): 125-135.
  6. Tur AO. Advanced computer vision for smart retail: From anomaly detection to fine-grained product classification. 2025.
  7. Chen B, Li L, Wang Y, Paiva RP, Lin Y, Yang X, et al. RetailDet: An efficient fusion attention network for joint product and vacancy identification in smart retail. IEEE Access 2025.
  8. Zangana H. Object detection and visual intelligence in retail environments: A deep learning approach for inventory and behavior analytics. Intelligent Methods in Engineering Sciences 2025; 4(2): 22-28.
  9. Manoj A, Ram NS, Venkatasubramanian K. Smart retail billing system using YOLOv10 object detection and Raspberry Pi. In: Proceedings of the 5th International Conference on Expert Clouds and Applications (ICOECA). IEEE; 2025: 1085-1093.
  10. Hofman O, Giloni A, Hayun Y, Morikawa I, Shimizu T, Elovici Y, et al. X-detect: Explainable adversarial patch detection for object detectors in retail. Machine Learning 2024; 113(9): 6273-6292.
  11. Ghosh R. Product identification in retail stores by combining Faster R-CNN and recurrent neural network. Multimedia Tools and Applications 2024; 83(3): 7135-7158.
  12. Vaneeta M, Mugada S, Patil PB, Preethi VJ, Deeksha K. Real-time object detection for an intelligent retail checkout system. In: Proceedings of the International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE; 2025: 1-6.
  13. Jocher G, Chaurasia A, Qiu J. YOLO by Ultralytics. 2023. Available at: https://github.com/ultralytics/ultralytics (accessed 24.08.2026).
  14. Wang CY, Yeh IH, Liao HY. YOLOv9: Learning what you want to learn using programmable gradient information. In: Computer Vision – ECCV 2024. Springer; 2024: 1-21.
  15. Skinderowicz R. Solving automatic check-out with fine-tuned YOLO models. Procedia Computer Science 2024; 246: 1649-1658.
  16. Jiang T, Zhong Y. ODverse33: Is the new YOLO version always better? A multi-domain benchmark from YOLOv5 to YOLOv11. arXiv preprint arXiv:2502.14314; 2025. DOI: 10.48550/arXiv.2502.14314.
  17. Zhang H, Li F, Liu S, Zhang L, Su H, Zhu J, et al. DINO: DETR with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605; 2022. DOI: 10.48550/arXiv.2203.03605.
  18. Wang S, Xia C, Lv F, Shi Y. RT-DETRv3: Real-time end-to-end object detection with hierarchical dense positive supervision. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. IEEE; 2025: 1628-1636.
  19. Mittal P. A comprehensive survey of deep learning-based lightweight object detection models for edge devices. Artificial Intelligence Review 2024; 57: 242.
  20. Zhao Y, Solihin MI, Yang D, Cai B, Chow LS, Handayani D, et al. LSR-YOLO: A lightweight and fast model for retail products detection. PLoS One 2025; 20: e0334216.
  21. Tur AO, Conti A, Beyan C, Boscaini D, Larcher R, Messelodi S, et al. Exploring fine-grained retail product discrimination with zero-shot object classification using vision-language models. In: Proceedings of the IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI). IEEE; 2024: 97-102.
  22. Li Y. Investigation of You Only Look Once networks for vision-based small object detection. International Journal of Advanced Computer Science and Applications 2023; 14(4).
  23. Jewellery. JewelleryDataset_OnlyStorePics computer vision model [dataset]. Roboflow Universe. Available at: https://universe.roboflow.com/jewellery/jewellerydataset_onlystorepics/dataset/3/images (accessed 24.08.2026).
  24. RangeKing. YOLOv8 [software configuration]. GitHub repository. Available at: https://github.com/open-mmlab/mmyolo/blob/dev/configs/yolov8/README.md (accessed 24.08.2026).

151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20