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Car logo recognition using YOLOv8 and microsoft azure custom vision

This research is conducted with its main objective to develop an accurate and faster model that can identify brands from logos captured through car images used by Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) staff. The software used for this case study is You Only Look Once (YOLO) version 8...

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Bibliographic Details
Main Authors: Muhammad ‘Arif, Mohd Anuwa, Nor Azuana, Ramli, Mohd Zaid Waqiyuddin, Mohd Zulkifli
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2023
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/42512/1/Car_Logo_Recognition_using_YOLOv8_and_Microsoft_Azure_Custom_Vision.pdf
http://umpir.ump.edu.my/id/eprint/42512/7/Car%20logo%20recognition%20using%20YOLOv8_ABST.pdf
http://umpir.ump.edu.my/id/eprint/42512/
https://doi.org/10.1109/ICDABI60145.2023.10629291
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spelling my.ump.umpir.425122024-09-06T03:22:57Z http://umpir.ump.edu.my/id/eprint/42512/ Car logo recognition using YOLOv8 and microsoft azure custom vision Muhammad ‘Arif, Mohd Anuwa Nor Azuana, Ramli Mohd Zaid Waqiyuddin, Mohd Zulkifli QA75 Electronic computers. Computer science This research is conducted with its main objective to develop an accurate and faster model that can identify brands from logos captured through car images used by Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) staff. The software used for this case study is You Only Look Once (YOLO) version 8 and Microsoft Azure's Custom Vision. Each software was compared and results from the analysis showed that YOLOv8 is renowned for its speed and efficiency and is capable of real-time object detection, which makes it ideal for applications where speed is critical. However, this approach might occasionally compromise accuracy, especially for smaller objects or objects that are close together. Microsoft Azure Custom Vision, on the other hand, may not be as fast as YOLOv8, but it generally delivers high accuracy, especially if adequately trained with a diverse set of tagged images. To conclude, the choice between YOLOv8 and Microsoft Azure Custom Vision depends on the specific requirements of the project, technical expertise, and resources. IEEE 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42512/1/Car_Logo_Recognition_using_YOLOv8_and_Microsoft_Azure_Custom_Vision.pdf pdf en http://umpir.ump.edu.my/id/eprint/42512/7/Car%20logo%20recognition%20using%20YOLOv8_ABST.pdf Muhammad ‘Arif, Mohd Anuwa and Nor Azuana, Ramli and Mohd Zaid Waqiyuddin, Mohd Zulkifli (2023) Car logo recognition using YOLOv8 and microsoft azure custom vision. In: 4th International Conference on Data Analytics for Business and Industry, ICDABI 2023 , 25 - 27 October 2023 , Bahrain. 477 -481.. ISBN 979-835036978-6 (Published) https://doi.org/10.1109/ICDABI60145.2023.10629291
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Muhammad ‘Arif, Mohd Anuwa
Nor Azuana, Ramli
Mohd Zaid Waqiyuddin, Mohd Zulkifli
Car logo recognition using YOLOv8 and microsoft azure custom vision
description This research is conducted with its main objective to develop an accurate and faster model that can identify brands from logos captured through car images used by Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) staff. The software used for this case study is You Only Look Once (YOLO) version 8 and Microsoft Azure's Custom Vision. Each software was compared and results from the analysis showed that YOLOv8 is renowned for its speed and efficiency and is capable of real-time object detection, which makes it ideal for applications where speed is critical. However, this approach might occasionally compromise accuracy, especially for smaller objects or objects that are close together. Microsoft Azure Custom Vision, on the other hand, may not be as fast as YOLOv8, but it generally delivers high accuracy, especially if adequately trained with a diverse set of tagged images. To conclude, the choice between YOLOv8 and Microsoft Azure Custom Vision depends on the specific requirements of the project, technical expertise, and resources.
format Conference or Workshop Item
author Muhammad ‘Arif, Mohd Anuwa
Nor Azuana, Ramli
Mohd Zaid Waqiyuddin, Mohd Zulkifli
author_facet Muhammad ‘Arif, Mohd Anuwa
Nor Azuana, Ramli
Mohd Zaid Waqiyuddin, Mohd Zulkifli
author_sort Muhammad ‘Arif, Mohd Anuwa
title Car logo recognition using YOLOv8 and microsoft azure custom vision
title_short Car logo recognition using YOLOv8 and microsoft azure custom vision
title_full Car logo recognition using YOLOv8 and microsoft azure custom vision
title_fullStr Car logo recognition using YOLOv8 and microsoft azure custom vision
title_full_unstemmed Car logo recognition using YOLOv8 and microsoft azure custom vision
title_sort car logo recognition using yolov8 and microsoft azure custom vision
publisher IEEE
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/42512/1/Car_Logo_Recognition_using_YOLOv8_and_Microsoft_Azure_Custom_Vision.pdf
http://umpir.ump.edu.my/id/eprint/42512/7/Car%20logo%20recognition%20using%20YOLOv8_ABST.pdf
http://umpir.ump.edu.my/id/eprint/42512/
https://doi.org/10.1109/ICDABI60145.2023.10629291
_version_ 1822924654041366528
score 13.235362

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