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Open access Jurnal SINTA 2

Optimizing Coral Fish Detection: Faster R-CNN, SSD MobileNet, YOLOv5 Comparison

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 18 No. 2 (2024)

DOI
10.22146/ijccs.95011
ISSN
2460-7258, 1978-1520
Published
2024-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This study underscores the critical role of accurate Chaetodontidae fish abundance observations, particularly in assessing coral reef health. By integrating deep learning algorithms (Faster R-CNN, SSD-MobileNet, and YOLOv5) into Autonomous Underwater Vehicles (AUVs), the research aims to expedite fish identification in aquatic environments. Evaluating the algorithms, YOLOv5 emerges with the highest accuracy, followed by Faster R-CNN and SSD-MobileNet. Despite this, SSD-MobileNet showcases superior computational speed with a mean average precision (mAP) of around 92.21% and a framerate of about 1.24 fps. Furthermore, employing the Coral USB Accelerator enhances computational speed on the Raspberry Pi 4, enabling real-time detection capabilities. This study incorporates centroid tracking, facilitating accurate counting by assigning unique IDs to identified objects per class. Ultimately, the real-time implementation of the system achieves 87.18% accuracy and 87.54% precision at 30 fps, empowering AUVs to conduct real-time fish detection and tracking, thereby significantly contributing to underwater research and conservation efforts.

Keywords

Collaboration network of the authors

5 authors · 6 co-authorship links

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

  • IPB University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Husnul KhatimahIPB University1
Agus Saleh AtmadipoeraIPB University1

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) 
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	<dc:title xml:lang="en-US">Optimizing Coral Fish Detection: Faster R-CNN, SSD MobileNet, YOLOv5 Comparison</dc:title>
	<dc:creator>Santoso, Syifa Afnani</dc:creator>
	<dc:creator>Jaya, Indra</dc:creator>
	<dc:creator>Priandana, Karlisa</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Object detection; Faster R-CNN; SSD MobileNet; YOLOv5; Centroid tracking</dc:subject>
	<dc:description xml:lang="en-US">This study underscores the critical role of accurate Chaetodontidae fish abundance observations, particularly in assessing coral reef health. By integrating deep learning algorithms (Faster R-CNN, SSD-MobileNet, and YOLOv5) into Autonomous Underwater Vehicles (AUVs), the research aims to expedite fish identification in aquatic environments. Evaluating the algorithms, YOLOv5 emerges with the highest accuracy, followed by Faster R-CNN and SSD-MobileNet. Despite this, SSD-MobileNet showcases superior computational speed with a mean average precision (mAP) of around 92.21% and a framerate of about 1.24 fps. Furthermore, employing the Coral USB Accelerator enhances computational speed on the Raspberry Pi 4, enabling real-time detection capabilities. This study incorporates centroid tracking, facilitating accurate counting by assigning unique IDs to identified objects per class. Ultimately, the real-time implementation of the system achieves 87.18% accuracy and 87.54% precision at 30 fps, empowering AUVs to conduct real-time fish detection and tracking, thereby significantly contributing to underwater research and conservation efforts.</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>2024-04-30</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/95011</dc:identifier>
	<dc:identifier>10.22146/ijccs.95011</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 18, No 2 (2024): April</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/95011/39105</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>