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Design Features of Grocery Product Recognition Using Deep Learning

E. Gothai1,*, Surbhi Bhatia2, Aliaa M. Alabdali3, Dilip Kumar Sharma4, Bhavana Raj Kondamudi5, Pankaj Dadheech6

1 Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, 638060, Tamil Nadu, India
2 Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Riyadh, 11533, Saudi Arabia
3 Faculty of Computing & Information Technology, King Abdulaziz University, Rabigh, 21911, Saudi Arabia
4 Department of Mathematics, Jaypee University of Engineering and Technology, Guna, 473226, Madhya Pradesh, India
5 Department of Management Studies, Institute of Public Enterprise, Hyderabad, 500101, Telangana, India
6 Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, 302017, Rajasthan, India

* Corresponding Author: E. Gothai. Email: email

Intelligent Automation & Soft Computing 2022, 34(2), 1231-1246.


At a grocery store, product supply management is critical to its employee's ability to operate productively. To find the right time for updating the item in terms of design/replenishment, real-time data on item availability are required. As a result, the item is consistently accessible on the rack when the client requires it. This study focuses on product display management at a grocery store to determine a particular product and its quantity on the shelves. Deep Learning (DL) is used to determine and identify every item and the store's supervisor compares all identified items with a preconfigured item planning that was done by him earlier. The approach is made in II-phases. Product detection, followed by product recognition. For product detection, we have used You Only Look Once Version 5 (YOLOV5), and for product recognition, we have used both the shape and size features along with the color feature to reduce the false product detection. Experimental results were carried out using the SKU-110 K data set. The analyses show that the proposed approach has improved accuracy, precision, and recall. For product recognition, the inclusion of color feature enables the reduction of error date. It is helpful to distinguish between identical logo which has different colors. We can achieve the accuracy percentage for feature level as 75 and score level as 81.


Cite This Article

APA Style
Gothai, E., Bhatia, S., Alabdali, A.M., Sharma, D.K., Kondamudi, B.R. et al. (2022). Design features of grocery product recognition using deep learning. Intelligent Automation & Soft Computing, 34(2), 1231-1246.
Vancouver Style
Gothai E, Bhatia S, Alabdali AM, Sharma DK, Kondamudi BR, Dadheech P. Design features of grocery product recognition using deep learning. Intell Automat Soft Comput . 2022;34(2):1231-1246
IEEE Style
E. Gothai, S. Bhatia, A.M. Alabdali, D.K. Sharma, B.R. Kondamudi, and P. Dadheech "Design Features of Grocery Product Recognition Using Deep Learning," Intell. Automat. Soft Comput. , vol. 34, no. 2, pp. 1231-1246. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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