Efficient YOLO-based models for real-time ceramic crack detection

Maungmeesri, Benchalak; Khonthon, Sasithorn; Maneetham, Dechrit; Crisnapati, Padma Nyoman

doi:10.11591/ijai.v15.i1.pp852-860

Open access Journal SINTA 1

Efficient YOLO-based models for real-time ceramic crack detection

IAES International Journal of Artificial Intelligence · Vol. 15 No. 1 (2026) · pp. 852–860

DOI: 10.11591/ijai.v15.i1.pp852-860
ISSN: 2252-8938, 2089-4872
Published: 2026-02-01
Publisher: Institute of Advanced Engineering and Science
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

The following research work systematically compares four variants of you only look once (YOLO), namely, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 proposed recently, considering the key properties required to perform ceramic surface crack detection tasks with high computational efficiency, real-time inference speed, and low memory usage. A total of 300 images of ceramic surfaces were collected with manually labeled cracks and divided into training, validation, and testing sets in portions of 263, 22, and 15 images, respectively. Each of the four YOLO variants was trained for 50 and 100 epochs, and each was evaluated regarding mean average precision (mAP), inference time, model size, and computational complexity in giga floating point operations per second (GFLOPs). YOLOv9 produced the highest accuracy with mAP values as high as 0.752-0.79 but the highest cost in terms of increased computational complexity. However, among these methods, YOLOv8 can produce the fastest inference (~2-2.3 ms) with a small memory footprint (~6 MB) with an acceptable accuracy of ~0.65-0.67. The results showed that YOLOv8 is the most feasible to deploy in resource constrained industrial automation environments. By offering this comparative study, the research attempts to provide hints for the selection of appropriate YOLO-based models by practitioners in quality control applications related to ceramic manufacturing.

Keywords

Collaboration network of the authors

18 authors · 44 co-authorship links · 9 across different organisations

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Rajamangala University of Technology
Institut Teknologi Dan Bisnis STIKOM Bali
Institut Teknologi Sepuluh Nopember
Sanata Dharma University
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
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Co-author	Organisation	Joint articles
Yamin Thwe	Rajamangala University of Technology	5
Evi Triandini	Institut Teknologi Dan Bisnis STIKOM Bali	4
Yuri Pamungkas	Institut Teknologi Sepuluh Nopember	3
Elang Parikesit	Rajamangala University of Technology	2
I Komang Agus Ady Aryanto	Rajamangala University of Technology	2
Petrus Sutyasadi	Sanata Dharma University	1
Nipat Jongsawat	Rajamangala University of Technology	1
Anucha Tungkasthan	Rajamangala University of Technology	1
Ratri Dwi Indriani	Affiliation unknown	1
Stralen Pratasik	Affiliation unknown	1
Made Krisnanda	Affiliation unknown	1
ABDUL KARIM	Affiliation unknown	1
Gao Yulan	Affiliation unknown	1
Myo Min Aung	Affiliation unknown	1

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) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">Efficient YOLO-based models for real-time ceramic crack detection</dc:title>
	<dc:creator>Maungmeesri, Benchalak</dc:creator>
	<dc:creator>Khonthon, Sasithorn</dc:creator>
	<dc:creator>Maneetham, Dechrit</dc:creator>
	<dc:creator>Crisnapati, Padma Nyoman</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Ceramic crack detection; Low-memory devices; Object detection; Real-time processing; You only look once</dc:subject>
	<dc:description xml:lang="en-US">The following research work systematically compares four variants of you only look once (YOLO), namely, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 proposed recently, considering the key properties required to perform ceramic surface crack detection tasks with high computational efficiency, real-time inference speed, and low memory usage. A total of 300 images of ceramic surfaces were collected with manually labeled cracks and divided into training, validation, and testing sets in portions of 263, 22, and 15 images, respectively. Each of the four YOLO variants was trained for 50 and 100 epochs, and each was evaluated regarding mean average precision (mAP), inference time, model size, and computational complexity in giga floating point operations per second (GFLOPs). YOLOv9 produced the highest accuracy with mAP values as high as 0.752-0.79 but the highest cost in terms of increased computational complexity. However, among these methods, YOLOv8 can produce the fastest inference (~2-2.3 ms) with a small memory footprint (~6 MB) with an acceptable accuracy of ~0.65-0.67. The results showed that YOLOv8 is the most feasible to deploy in resource constrained industrial automation environments. By offering this comparative study, the research attempts to provide hints for the selection of appropriate YOLO-based models by practitioners in quality control applications related to ceramic manufacturing.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
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	<dc:date>2026-02-01</dc:date>
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	<dc:identifier>10.11591/ijai.v15.i1.pp852-860</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 1: February 2026; 852-860</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v15.i1</dc:source>
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	<dc:rights xml:lang="en-US">Copyright (c) 2026 Benchalak Maungmeesri, Sasithorn Khonthon, Dechrit Maneetham, Padma Nyoman Crisnapati</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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