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Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales

IAES International Journal of Artificial Intelligence · Vol. 13 No. 3 (2024) · hlm. 3291–3305

DOI
10.11591/ijai.v13.i3.pp3291-3305
ISSN
2252-8938, 2089-4872
Terbit
2024-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

You only look once v8 (YOLOv8)-seg and its variants are implemented to accelerate the collection of goods for a store for selling activity in Indonesia. The method used here is object detection and segmentation of these objects, a combination of detection and segmentation called instance segmentation. The novelty lies in the customization and optimization of YOLOv8-seg for detecting and segmenting 30 specific Indonesian products. The use of augmented data (125 images augmented into 1,250 images) enhances the model's ability to generalize and perform well in various scenarios. The small number of data points and the small number of epochs have proven reliable algorithms to implement on store products instead of using QR codes in a digital manner. Five models are examined, i.e., YOLOv8-seg, YOLOv8s-seg, YOLOv8m-seg, YOLOv8l-seg, and YOLOv8x-seg, with a data distribution of 64% for the training dataset, 16% for the validation dataset, and 20% for the testing dataset. The best model, YOLOv8l-seg, was obtained with the highest mean average precision (mAP) box value of 99.372% and a mAPmask value of 99.372% from testing the testing dataset. However, the YOLOv8mseg model can be the best alternative model with a mAPbox value of 99.330% since the number of parameters and the computational speed are the best compared to other models.

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  • Satya Wacana Christian University
  • Universiti Malaysia Sarawak
  • Bandung Institute of Technology
  • Universiti Putra Malaysia
  • National Research and Innovation Agency
  • Universiti Teknologi MARA
  • Universitas Pembangunan Nasional Veteran Yogyakarta
  • Afiliasi tidak diketahui
  • Organisasi berbeda
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  • Afiliasi tidak diketahui
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Suryasatriya TrihandaruAfiliasi tidak diketahui4
Didit Budi NugrohoSatya Wacana Christian University4
Adi SetiawanSatya Wacana Christian University2
Yosia Adi SusetyoAfiliasi tidak diketahui2
Bambang SusantoNational Research and Innovation Agency2
Yosia Adi SusetyoSatya Wacana Christian University2
Abdiel Wilyar GoniAfiliasi tidak diketahui2
Marthen Sattu SamboSatya Wacana Christian University2
Yunita Puput WijayantiSatya Wacana Christian University1
FachrurroziAfiliasi tidak diketahui1
Wynona Adita PradaniAfiliasi tidak diketahui1
Hindriyanto Dwi PurnomoSatya Wacana Christian University1
Istiarsi Saptuti Sri KawuryanSatya Wacana Christian University1
Happy Alyzhya HaaySatya Wacana Christian University1
Bambang SusantoSatya Wacana Christian University1
Nur I. M. UrosidinSatya Wacana Christian University1
Yohannes SardjonoAfiliasi tidak diketahui1
Isman Mulyadi TriatmokoAfiliasi tidak diketahui1
Gede Sutresna WijayaAfiliasi tidak diketahui1
Jane LabadinAfiliasi tidak diketahui1
Nurul Syuhada IsmailUniversiti Malaysia Sarawak1
Norhunaini Mohd ShaipullahUniversiti Malaysia Sarawak1
Rusya Iryanti YahayaUniversiti Putra Malaysia1
Nur Fazliana RahimUniversiti Teknologi MARA1
Norihan Md AriffinUniversiti Putra Malaysia1
Ivanky SaputraAfiliasi tidak diketahui1
Septoratno SiregarBandung Institute of Technology1
Leksono MucharamBandung Institute of Technology1
Harry Budiharjo SulistyarsoUniversitas Pembangunan Nasional Veteran Yogyakarta1
Sinatra CanggihBandung Institute of Technology1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales</dc:title>
	<dc:creator>Arini Parhusip, Hanna</dc:creator>
	<dc:creator>Trihandaru, Suryasatriya</dc:creator>
	<dc:creator>Indrajaya, Denny</dc:creator>
	<dc:creator>Labadin, Jane</dc:creator>
	<dc:subject xml:lang="en-US">Deep Learning; Neural Network; Artificial Intelligence</dc:subject>
	<dc:subject xml:lang="en-US">Deep learning; Instance segementation; Point of sales; Store products; You only look once v8-seg</dc:subject>
	<dc:description xml:lang="en-US">You only look once v8 (YOLOv8)-seg and its variants are implemented to accelerate the collection of goods for a store for selling activity in Indonesia. The method used here is object detection and segmentation of these objects, a combination of detection and segmentation called instance segmentation. The novelty lies in the customization and optimization of YOLOv8-seg for detecting and segmenting 30 specific Indonesian products. The use of augmented data (125 images augmented into 1,250 images) enhances the model's ability to generalize and perform well in various scenarios. The small number of data points and the small number of epochs have proven reliable algorithms to implement on store products instead of using QR codes in a digital manner. Five models are examined, i.e., YOLOv8-seg, YOLOv8s-seg, YOLOv8m-seg, YOLOv8l-seg, and YOLOv8x-seg, with a data distribution of 64% for the training dataset, 16% for the validation dataset, and 20% for the testing dataset. The best model, YOLOv8l-seg, was obtained with the highest mean average precision (mAP) box value of 99.372% and a mAPmask value of 99.372% from testing the testing dataset. However, the YOLOv8mseg model can be the best alternative model with a mAPbox value of 99.330% since the number of parameters and the computational speed are the best compared to other models.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">The research here is supported by internal university funding from Satya Wacana Christian University in Indonesia, No. 146/SPK-PT/RIK/9/2023 for producing the data.</dc:contributor>
	<dc:date>2024-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24698</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i3.pp3291-3305</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 3: September 2024; 3291-3305</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/24698/14138</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>