Point of Sale System Using Convolutional Neural Network for Image Recognition in Grocery Store
DOI:
Abstract
The history of point of sale already has been told from a long time ago. The business nowadays is opting for the point-of-sale transactions because it was easy to sell the item to people face to face. This will build some trust between the cashier and the customer. The popular store that always customer need was the grocery store. However, the grocery store nowadays still not has a good feature for the point-of-sale system. The cashier still needs to scan the item through barcode scanner. This idea was led to make the point-of-sale transactions easier in the grocery store by applying the machine learning to the system. The problem for this project is the customer wait for a long time for their point-of-sale transactions to finish when bought the grocery items. The aim of this project is to detect the grocery items with convolutional neural network model for image recognition through camera within the main user interface. The Agile Development Life Cycle (ADLC) method is used in the development of Point-of-Sale System using Machine Learning for Image Recognition in Grocery Store. Moreover, this project is to evaluate the usability of the system using Post-Study System Usability Questionnaire (PSSUQ) approach. The PSSUQ evaluation is evaluated by the users of the system. The results of PSSUQ stated that the users satisfied with the system. The future research for this project is to make the point-of-sale system with a better model in the future. In conclusion, the system is works well and machine learning image recognition model also can detect the grocery item clearly.
Downloads
Views : 206
Downloads : 123
References
A. Ioannidou, E. Chatzilari, S. Nikolopoulos, and L. Field, “Online Appendix to: Deep Learning Advances in Computer Vision with 3D Data: A survey,” ACM computing surveys (CSUR), vol. 50, no. 2, pp.1-38. 2017.
M. Leo, P. Carcagnì, and C. Distante, “A systematic investigation on end-to-end deep recognition of grocery products in the wild,” Proceedings - International Conference on Pattern Recognition, pp. 7234–7241, 2020.
Y. Liu, B. Liu, and Y. Chen, “Research on Image Recognition of Supermarket Commodity Based on Convolutional Neural Network,” Proceedings - 2019 12th International Symposium on Computational Intelligence and Design, ISCID 2019, vol. 1, no. 3, pp. 171–174, 2019.
P. Rani, S. Kotwal, J. Manhas, V. Sharma, and S. Sharma, “Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments,” In Archives of Computational Methods in Engineering. Springer Netherlands, vol. 29, no. 3, pp. 1801-1837, 2022.
K. Wankhede, B. Wukkadada, and V. Nadar, “Just Walk-Out Technology and its Challenges: A Case of Amazon Go,” Proceedings of the International Conference on Inventive Research in Computing Applications, pp. 254–257, 2018.
A. Camps, O. Supervisora, and B. Otero, (n.d). Degree thesis Design and implementation of a mobile application for image recognition and its managing tool Under the following conditions.
H. Chen, Z. He, B. Shi, and T. Zhong, “Research on Recognition Method of Electrical Components Based on YOLO V3,” IEEE Access, vol. 7, pp. 157818–157829, 2019.
Y. Konishi, Y. Hanzawa, M. Kawade, and M. Hashimoto, “Fast 6D Pose Estimation Using Hierarchical Pose Trees,” Eccv, vol. 1, pp. 398–413, 2016.
E. Y. Li, “Amazon Go concept,” Journal of Business and Management, vol. 24, no. 1, pp. 79-92, 2018.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017.
A. Svyrydov, H. Kuchuk, and O. Tsiapa, “Improving efficienty of image recognition process: Approach and case study,” Proceedings of 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies, DESSERT 2018, pp. 593– 597, 2018.
N. Syam, and A. Sharma, “Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice,” Industrial Marketing Management, 69 (November 2017), pp. 135–146, 2018.
ArcGIS API for Python. How single-shot detector (SSD) works? | ArcGIS Developer. (n.d.). [Online]. Available: https: //developers.arcgis.com/python/guide/how- ssd- works/. [Accessed: December 10, 2021].
R. Gandhi. (2021). R-CNN, fast R-CNN, Faster R-CNN, YOLO - object detection algorithms. Medium.
Introduction to yolo algorithm for object detection. Section. (n.d.). [Online]. Available: https: //www.section.io/engineering-education/introduction-to-yolo- algorithm-for- object-detection/. [Accessed: December 10, 2021].
Mobile POS app with visual recognition. MOLO17. (Nov, 2020). [Online]. Available: https: //molo17.com/mobile-pos-app-with-visual- recognition/. [Accessed: December 10, 2021].
A complete guide to image recognition. Nanonets. (n.d.). [Online]. Available: https: //nanonets.com/image-recognition. [Accessed: December 10, 2021].
C. Gacek, A. Abd-Allah, B. Clark, and B. W. Boehm, “On the Definition of Software System Architecture,” The First International Workshop on Architectures for Software Systems, pp. 85-95, 1995.
S. Radack, “Security Considerations in the System Development Life Cycle,” National Institute of Standards and Technology, pp. 1-7, 2002.
S. Sharma, and S. K. Pandey, “Integrating AI techniques in SDLC: Design phase perspective,” ACM International Conference Proceeding Series, pp. 383–387, August 2015.
S. Yun, J. Choi, D. P. de Oliveira, and S. P. Mulva, “Development of performance metrics for phase-based capital research benchmarking,” International Journal of Research Management, vol. 34, no. 3, pp. 389–402, 2016.
A. S. Jadhav, and R. M. Sonar, “Evaluating and selecting software packages: A review,” Information and Software Technology, vol. 51. no. 3, pp. 555–563, 2009.
