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Open access Jurnal SINTA 2

Object Detection Based on You Look Only Once Version 8 for Real-Time Applications

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 18 No. 3 (2024)

DOI
10.22146/ijccs.94843
ISSN
2460-7258, 1978-1520
Published
2024-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This research focus to involves human detection in crowded situations, especially in the lecturer's room. The lecturer's room is very vulnerable because it can be accessed by anyone with only one entry and exit to the lecturer's room, so it would be perfect to place this Yolo camera in front of the lecturer's room so that incoming and outgoing activities can be monitored during work days on campus. The main challenge is how the system can distinguish individuals in dense crowds and identify their relative locations to each other. In this context, it is necessary to find a solution that can overcome the uncertainty of recognizing individuals in a group and accurately understand the location and distance between them. One proposed solution is to use the YOLO algorithm on video recordings to detect human objects in the lecturer's room during working hours. This research introduces the YOLOv8 model, a real-time detection system with high speed and accuracy in detecting and classifying objects in video recordings. YOLOv8 can accurately detect object movement, making it an efficient real-time framework for dealing with complex objects. This research experiment involved using eight different smartphone devices to collect datasets. Using various smartphone devices aims to test object detection performance under various shooting conditions, including variations in image quality, lighting, shooting angle, and camera resolution. The research results show that using multiple smartphone devices in dataset collection can improve the robustness and accuracy of object detection models. By integrating datasets from various sources and shooting conditions, the YOLOv8 model was successfully trained to better recognize objects in different situations, even in campus environments that often have challenges such as weather variations and lighting fluctuations. The test results show an accuracy rate of 93.33% in human object detection

Keywords

Collaboration network of the authors

12 authors · 22 co-authorship links · 1 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Bisnis dan Teknologi Indonesia
  • Universitas Mercu Buana Yogyakarta
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
I Gede Iwan SudipaInstitut Bisnis dan Teknologi Indonesia3
I Komang Arya Ganda WigunaInstitut Bisnis dan Teknologi Indonesia3
Jamiin Al Yastawil YastawilInstitut Bisnis dan Teknologi Indonesia2
Kadek Nonik ErawatiInstitut Bisnis dan Teknologi Indonesia2
I Gede Made Yudi AntaraInstitut Bisnis dan Teknologi Indonesia2
I Made YudianaAffiliation unknown1
Paholo Iman PrakosoUniversitas Mercu Buana Yogyakarta1
I Putu Yudi PratamaInstitut Bisnis dan Teknologi Indonesia1
I Kadek Agus DwipayanaInstitut Bisnis dan Teknologi Indonesia1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Object Detection Based on You Look Only Once Version 8  for Real-Time Applications</dc:title>
	<dc:creator>Santiago, Gede Agus</dc:creator>
	<dc:creator>Sugiartawan, Putu</dc:creator>
	<dc:creator>Ardriani, Ni Nengah Dita</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Object detection; human detection;  YOLOV8; Accuration;</dc:subject>
	<dc:description xml:lang="en-US">This research focus to involves human detection in crowded situations, especially in the lecturer's room. The lecturer's room is very vulnerable because it can be accessed by anyone with only one entry and exit to the lecturer's room, so it would be perfect to place this Yolo camera in front of the lecturer's room so that incoming and outgoing activities can be monitored during work days on campus. The main challenge is how the system can distinguish individuals in dense crowds and identify their relative locations to each other. In this context, it is necessary to find a solution that can overcome the uncertainty of recognizing individuals in a group and accurately understand the location and distance between them. One proposed solution is to use the YOLO algorithm on video recordings to detect human objects in the lecturer's room during working hours. This research introduces the YOLOv8 model, a real-time detection system with high speed and accuracy in detecting and classifying objects in video recordings. YOLOv8 can accurately detect object movement, making it an efficient real-time framework for dealing with complex objects. This research experiment involved using eight different smartphone devices to collect datasets. Using various smartphone devices aims to test object detection performance under various shooting conditions, including variations in image quality, lighting, shooting angle, and camera resolution. The research results show that using multiple smartphone devices in dataset collection can improve the robustness and accuracy of object detection models. By integrating datasets from various sources and shooting conditions, the YOLOv8 model was successfully trained to better recognize objects in different situations, even in campus environments that often have challenges such as weather variations and lighting fluctuations. The test results show an accuracy rate of 93.33% in human object detection</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-07-31</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://journal.ugm.ac.id/ijccs/article/view/94843</dc:identifier>
	<dc:identifier>10.22146/ijccs.94843</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 18, No 3 (2024): July</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/94843/39739</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>