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

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 segmentati...

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Main Authors: Hanna Arini, Parhusip, Suryasatriya, Trihandaru, Denny, Indrajaya, Jane, Labadin
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU) 2024
Subjects:
QA Mathematics
Online Access:http://ir.unimas.my/id/eprint/46860/1/24698-52847-1-PB.pdf
http://ir.unimas.my/id/eprint/46860/
https://ijai.iaescore.com/index.php/IJAI/article/view/24698/14138
http://doi.org/10.11591/ijai.v13.i3.pp3291-3305
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spelling my.unimas.ir-468602024-12-10T02:27:20Z http://ir.unimas.my/id/eprint/46860/ Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales Hanna Arini, Parhusip Suryasatriya, Trihandaru Denny, Indrajaya Jane, Labadin QA Mathematics 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. Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU) 2024 Article PeerReviewed text en http://ir.unimas.my/id/eprint/46860/1/24698-52847-1-PB.pdf Hanna Arini, Parhusip and Suryasatriya, Trihandaru and Denny, Indrajaya and Jane, Labadin (2024) Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales. IAES International Journal of Artificial Intelligence (IJ-AI), 13 (3). pp. 3291-3305. ISSN 2252-8938 https://ijai.iaescore.com/index.php/IJAI/article/view/24698/14138 http://doi.org/10.11591/ijai.v13.i3.pp3291-3305
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Hanna Arini, Parhusip
Suryasatriya, Trihandaru
Denny, Indrajaya
Jane, Labadin
Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales
description 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.
format Article
author Hanna Arini, Parhusip
Suryasatriya, Trihandaru
Denny, Indrajaya
Jane, Labadin
author_facet Hanna Arini, Parhusip
Suryasatriya, Trihandaru
Denny, Indrajaya
Jane, Labadin
author_sort Hanna Arini, Parhusip
title Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales
title_short Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales
title_full Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales
title_fullStr Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales
title_full_unstemmed Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales
title_sort implementation of yolov8-seg on store products to speed up the scanning process at point of sales
publisher Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU)
publishDate 2024
url http://ir.unimas.my/id/eprint/46860/1/24698-52847-1-PB.pdf
http://ir.unimas.my/id/eprint/46860/
https://ijai.iaescore.com/index.php/IJAI/article/view/24698/14138
http://doi.org/10.11591/ijai.v13.i3.pp3291-3305
_version_ 1818839389529178112
score 13.223943

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