Evaluating the Effectiveness of Panoptic Segmentation Through Comparative Analysis

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dc.contributor.authorSara, Cahide
dc.contributor.authorDaşdemir, İlhan
dc.contributor.authorYetgin, Salih Hakan
dc.date.accessioned2025-03-17T12:19:45Z
dc.date.available2025-03-17T12:19:45Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description.abstractImage segmentation method is extensively used in the fields of computer vision, machine learning, and artificial intelligence. The task of segmentation is to distinguish objects in images either by their boundaries or as entire objects from the entire image. Image segmentation methods are implemented as instance, semantic, and panoptic segmentation. In this article, the panoptic segmentation method, seen as an advanced stage of instance and semantic segmentation, has been applied to three datasets and compared with the instance segmentation method. Experimental results are presented visually. Numerical results have been analyzed with the Panoptic Quality (PQ) and Semantic Quality (SQ) metrics. It has been observed that the segmentation outcome was best for the CityScapes dataset for panoptic segmentation.
dc.identifier.doi10.17798/bitlisfen.1473041
dc.identifier.endpage691
dc.identifier.issn2147-3129
dc.identifier.issn2147-3188
dc.identifier.issue3
dc.identifier.startpage681
dc.identifier.trdizinid1267341
dc.identifier.urihttps://doi.org/10.17798/bitlisfen.1473041
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1267341
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1147
dc.identifier.volume13
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofBitlis Eren Üniversitesi Fen Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR_20250316
dc.subjectImage processing
dc.subjectimage segmentation
dc.subjectinstance segmentation
dc.subjectpanoptic segmentation
dc.titleEvaluating the Effectiveness of Panoptic Segmentation Through Comparative Analysis
dc.typeArticle

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