Development of a Hybrid Method for Multi-Stage End-to-End Recognition of Grocery Products in Shelf Images

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dc.WoS.categoriesComputer Science, Information SystemsEngineering, Electrical & ElectronicPhysics, Applieden_US
dc.authorid0000-0001-5795-4698en_US
dc.contributor.authorMelek, Ceren Gülra
dc.contributor.authorSönmez, Elena Battini
dc.contributor.authorAyral, Hakan
dc.contributor.authorVarlı, Songül
dc.date.accessioned2023-09-19T08:35:40Z
dc.date.available2023-09-19T08:35:40Z
dc.date.issued2023-09
dc.description.abstractAbstract: Product recognition on grocery shelf images is a compelling task of object detection because of the similarity between products, the presence of the different scale of product sizes, and the high number of classes, in addition to constantly renewed packaging and added new products’ difficulty in data collection. The use of conventional methods alone is not enough to solve a number of retail problems such as planogram compliance, stock tracking on shelves, and customer support. The purpose of this study is to achieve significant results using the suggested multi-stage end-toend process, including product detection, product classification, and refinement. The comparison of different methods is provided by a traditional computer vision approach, Aggregate Channel Features (ACF) and Single-Shot Detectors (SSD) are used in the product detection stage, and Speed-up Robust Features (SURF), Binary Robust Invariant Scalable Key points (BRISK), Oriented Features from Accelerated Segment Test (FAST), Rotated Binary Robust Independent Elementary Features (BRIEF) (ORB), and hybrids of these methods are used in the product classification stage. The experimental results used the entire Grocery Products dataset and its different subsets with a different number of products and images. The best performance was achieved with the use of SSD in the product detection stage and the hybrid use of SURF, BRISK, and ORB in the product classification stage, respectively. Additionally, the proposed approach performed comparably or better than existing modelsen_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.3390/electronics12173640en_US
dc.identifier.issn2079-9292
dc.identifier.scopus2-s2.0-85170376498en_US
dc.identifier.urihttps://hdl.handle.net/11411/5196
dc.identifier.urihttps://doi.org/10.3390/electronics12173640
dc.identifier.wosWOS:001061050600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.issue17en_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors3en_US
dc.publisherMDPIen_US
dc.relation.ispartofELECTRONICSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBRISKen_US
dc.subjectORBen_US
dc.subjectplanogram complianceen_US
dc.subjectproduct recognitionen_US
dc.subjectSSDen_US
dc.subjectSURFen_US
dc.titleDevelopment of a Hybrid Method for Multi-Stage End-to-End Recognition of Grocery Products in Shelf Imagesen_US
dc.typeArticleen_US
dc.volume12en_US

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