Investigating the Effects of Pre-Trained Deep Learning Models and Fusion Techniques on Fruit Segmentation Performance

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dc.contributor.authorİbiş, Esma
dc.contributor.authorUğur, Aybars
dc.date.accessioned2026-02-08T15:11:12Z
dc.date.available2026-02-08T15:11:12Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description9th International Conference on Computer Science and Engineering, UBMK 2024 -- 2024-10-26 through 2024-10-28 -- Antalya -- 204906
dc.description.abstractIn recent years, interest in the automation of agricultural and industrial processes has increased. Fruit segmentation is essential for quality control, harvest optimization, and yield estimation. However, some challenges arise in automatic fruit segmentation systems, such as light conditions, image quality, environmental factors, and variations in fruit appearance. To over-come these challenges, we proposed an approach by investigating the effects of pretrained deep learning models with late fusion techniques. Firstly, we designed a late fusion module focusing on the information fusion in the last two layers of the deep learning models. Then, we integrated this late fusion module into the two parallel deep learning models that process the input images independently. Finally, the fruit segmentation output is produced based on the combined information in the late fusion module. The proposed method achieved an IoU score of 79.13% on the Minneapple dataset. Compared to CNN and Transformer models, the proposed method achieved a performance improvement of 9.42% and 3.29%, respectively. © 2024 IEEE.
dc.identifier.doi10.1109/UBMK63289.2024.10773393
dc.identifier.endpage321
dc.identifier.isbn9798350365887
dc.identifier.scopus2-s2.0-85215532236
dc.identifier.scopusqualityN/A
dc.identifier.startpage316
dc.identifier.urihttps://doi.org/10.1109/UBMK63289.2024.10773393
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5316
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzScopus_KA_20260207
dc.subjectdeep learning
dc.subjectfruit segmentation
dc.subjectfusion techniques
dc.titleInvestigating the Effects of Pre-Trained Deep Learning Models and Fusion Techniques on Fruit Segmentation Performance
dc.typeConference Object

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