https://doi.org/10.1140/epjs/s11734-025-01775-w
Regular Article
Deep learning based automated non-barcoded product identification system for in-person shopping
1
Faculty of Technology, Department of Computer Engineering, Sakarya University of Applied Sciences, 54000, Serdivan, Sakarya, Turkey
2
Department of Computer and Information Engineering, Institute of Natural Sciences, Sakarya University, Serdivan, 54000, Sakarya, Turkey
3
Overtech Information Technologies, Yıldız Technical University, Teknopark, Fatih, 34000, Istanbul, Turkey
4
Faculty of Electrical and Electronics, Computer Engineering, Yıldız Technical University, Fatih, 34000, Istanbul, Turkey
Received:
13
March
2025
Accepted:
27
June
2025
Published online:
14
July
2025
Deep learning, an advanced extension of machine learning, is widely used for complex challenges. This study presents an automated system for recognizing non-barcoded products in retail environments, addressing inefficiencies in manual identification. A novel dataset, MAKBUL, was developed with 4500 RGB images from 30 product categories (150 images each), captured using a custom-designed camera system on a retail weighing platform. Experiments with six pre-trained CNNs showed that DenseNet201 achieved the highest individual accuracy (96.00%) when fine-tuned with a 0.00001 learning rate, RMSProp optimizer, and 100 epochs. Using feature extraction, EfficientNet + SVM reached 96.07% accuracy. The best result, 96.56%, was obtained through feature-level fusion, combining features from all six models. The study tested 27 hyperparameter combinations and applied 5-fold cross-validation for robustness. Findings highlight the efficiency of transfer learning in multi-class product recognition, reducing customer wait times and manual labor. This research lays the groundwork for future automated retail systems, with potential enhancements including larger datasets, varied lighting conditions, and additional product categories.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
