Datasets and methods of product recognition on grocery shelf images using computer vision and machine learning approaches: An exhaustive literature review

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dc.authorscopusid56545836200
dc.authorscopusid36782998200
dc.authorscopusid57478707800
dc.contributor.authorMelek, C.G.
dc.contributor.authorBattini Sönmez, E.
dc.contributor.authorVarlı, S.
dc.date.accessioned2024-07-18T20:16:49Z
dc.date.available2024-07-18T20:16:49Z
dc.date.issued2024
dc.description.abstractA product recognition system recognizes all products on the shelf images and determines their positions. A business equipped with an automatic product recognition system has a convenient follow-up of many human-powered activities while increasing customer satisfaction. That is, product recognition stands out with its benefits such as tracking shelf layouts and stocking their status, and improving the shopping experience for customers, especially the visually impaired ones. However, product recognition is a challenging problem of computer vision in terms of the difficulty of obtaining and updating datasets and the breadth of the product scale. On the other hand, the number of studies on product recognition is constantly increasing by using various computer vision and machine learning methods, and effective solutions are offered to this problem. This paper provide a comprehensive review in the field of researches of product recognition on grocery store shelves. In this article, data sets and approaches used in the literature for the development of an automatic product recognition system are examined and compared, and their benefits and limitations are commented. Finally, a guideline is provided for future researchers and new perspectives for future studies are presented. © 2024 Elsevier Ltden_US
dc.identifier.doi10.1016/j.engappai.2024.108452
dc.identifier.issn0952-1976
dc.identifier.scopus2-s2.0-85191897967en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2024.108452
dc.identifier.urihttps://hdl.handle.net/11411/6285
dc.identifier.volume133en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputer Visionen_US
dc.subjectGrocery Shelf İmagesen_US
dc.subjectMachine Learningen_US
dc.subjectPlanogram Complianceen_US
dc.subjectProduct Recognitionen_US
dc.subjectCustomer Satisfactionen_US
dc.subjectMachine Learningen_US
dc.subjectFollow Upen_US
dc.subjectGrocery Shelf İmageen_US
dc.subjectHuman-Powereden_US
dc.subjectLiterature Reviewsen_US
dc.subjectMachine Learning Approachesen_US
dc.subjectMachine-Learningen_US
dc.subjectPlanogram Complianceen_US
dc.subjectProduct Recognitionen_US
dc.subjectRecognition Systemsen_US
dc.subjectVision Learningen_US
dc.subjectComputer Visionen_US
dc.titleDatasets and methods of product recognition on grocery shelf images using computer vision and machine learning approaches: An exhaustive literature reviewen_US
dc.typeArticleen_US

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