Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 2

The Effect of Data Augmentation in Deep Learning with Drone Object Detection

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 3 (2023) · pp. 237–248

DOI
10.22146/ijccs.84785
ISSN
2460-7258, 1978-1520
Published
2023-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Drone object detection is one of the main applications of image processing technology and pattern recognition using deep learning. However, the limited drone image data that can be accessed for training detection algorithms is a challenge developing drone object detection technology. Therefore, many studies have been conducted to increase the amount of drone image data using data augmentation techniques. This study aims to evaluate the effect of data augmentation on deep learning accuracy in drone object detection using the YOLOv5 algorithm. The methods used in this research include collecting drone image data, augmenting data with rotate, crop, and cutout, training the YOLOv5 algorithm with and without data augmentation, as well as testing and analyzing training results.The results of the study show that data augmentation can't improve the accuracy of the YOLOv5 algorithm in drone object detection. Evidenced by the decreasing value of precision and mAP@0.5 and the relatively constant value of recall and F-1 score. This is caused by too much augmentation, which can cause a loss of important information in the data and cause noise or distortion.

Keywords

Collaboration network of the authors

23 authors · 45 co-authorship links · 15 across different organisations

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

  • Universitas Amikom Yogyakarta
  • Universitas Gadjah Mada
  • Universidad de Murcia
  • Hellenic Open University
  • Universitas Insan Mahardika
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Arief SetyantoUniversitas Amikom Yogyakarta2
Rodrigo Martínez‐BéjarUniversidad de Murcia2
I Made Artha AgastyaUniversitas Amikom Yogyakarta2
Risna SariUniversitas Amikom Yogyakarta1
Tonny HidayatUniversitas Amikom Yogyakarta1
Theofanis G. OrphanoudakisHellenic Open University1
Retzi Yosia LewuAffiliation unknown1
Ainul YaqinUniversitas Amikom Yogyakarta1
Agus HarjokoUniversitas Gadjah Mada1
Retantyo WardoyoUniversitas Gadjah Mada1
Sri T. HartatiUniversitas Gadjah Mada1
Syah Alam Afif SahubawaUniversitas Amikom Yogyakarta1
Andi SunyotoUniversitas Amikom Yogyakarta1
Ade PujiantoUniversitas Amikom Yogyakarta1
Ema UtamiUniversitas Amikom Yogyakarta1
Mardi Siswo UtomoAffiliation unknown1
Hanif Al FattaUniversitas Amikom Yogyakarta1
Muhammad Resa Arif YudiantoUniversitas Amikom Yogyakarta1
Alba Puelles LópezUniversidad de Murcia1
Mohamad FirdausUniversitas Insan Mahardika1

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">The Effect of Data Augmentation in Deep Learning with Drone Object Detection</dc:title>
	<dc:creator>Alin, Ariel Yonatan</dc:creator>
	<dc:creator>Kusrini, Kusrini</dc:creator>
	<dc:creator>Yuana, Kumara Ari</dc:creator>
	<dc:subject xml:lang="en-US">Machine Learning; Data Science</dc:subject>
	<dc:subject xml:lang="en-US">Data Augmentation; YOLOv5; Object detection; Drone detection</dc:subject>
	<dc:description xml:lang="en-US"> Drone object detection is one of the main applications of image processing technology and pattern recognition using deep learning. However, the limited drone image data that can be accessed for training detection algorithms is a challenge developing drone object detection technology. Therefore, many studies have been conducted to increase the amount of drone image data using data augmentation techniques. This study aims to evaluate the effect of data augmentation on deep learning accuracy in drone object detection using the YOLOv5 algorithm. The methods used in this research include collecting drone image data, augmenting data with rotate, crop, and cutout, training the YOLOv5 algorithm with and without data augmentation, as well as testing and analyzing training results.The results of the study show that data augmentation can't improve the accuracy of the YOLOv5 algorithm in drone object detection. Evidenced by the decreasing value of precision and mAP@0.5 and the relatively constant value of recall and F-1 score. This is caused by too much augmentation, which can cause a loss of important information in the data and cause noise or distortion.</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>2023-07-31</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/84785</dc:identifier>
	<dc:identifier>10.22146/ijccs.84785</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 3 (2023): July; 237-248</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/84785/36981</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 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>