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saLFIA: Semi-automatic Live Feeds Image Annotation Tool for Vehicle Classification Dataset

Journal of ICT Research and Applications (JICTRA) · Vol. 18 No. 2 (2024) · pp. 143–154

DOI
10.5614/itbj.ict.res.appl.2023.18.2.5
ISSN
2338-5499, 2337-5787
Published
2024-10-21
Publisher
DRI - ITB
Language
eng

Abstract

Deep learning’s reliance on abundant data with accurate annotations presents a significant drawback, as developing datasets is often time-consuming and costly for specific problems. To address this drawback, we propose a semi-automatic live-feed image annotation tool called saLFIA. Our case study utilized CCTV data from Indonesia’s toll roads as one of the sources for live-feed images. The primary contribution of saLFIA is a labeling tool designed to generate new datasets from public source images, focusing on vehicle classification using YOLOv3 and SSD algorithms. The evaluation results indicated that SSD achieved higher accuracy with fewer initial images, while YOLOv3 reached maximum accuracy with larger initial datasets, resulting in 8 misdetections out of 380 objects. The saLFIA tool simplifies the annotation process, presenting a labeling tool for creating annotated datasets in a single operation. saLFIA is available at URL https://github.com/gilangmantara/salfia.

Keywords

Collaboration network of the authors

7 authors · 16 co-authorship links · 1 across different organisations

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  • National Research and Innovation Agency
  • Universitas Teknologi Yogyakarta
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Zulfikar FebriantoUniversitas Teknologi Yogyakarta1

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://journals.itb.ac.id/index.php/jictra/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">saLFIA: Semi-automatic Live Feeds Image Annotation Tool for Vehicle Classification Dataset</dc:title>
	<dc:creator>Chasanah, Umi</dc:creator>
	<dc:creator>Putra, Gilang</dc:creator>
	<dc:creator>Bismantoko, Sahid</dc:creator>
	<dc:creator>Hidayat, Sofwan</dc:creator>
	<dc:creator>Widodo, Tri</dc:creator>
	<dc:creator>Rosyidi, Mohammad</dc:creator>
	<dc:subject xml:lang="en-US">annotation tool</dc:subject>
	<dc:subject xml:lang="en-US">CCTV</dc:subject>
	<dc:subject xml:lang="en-US">dataset</dc:subject>
	<dc:subject xml:lang="en-US">vehicle classification</dc:subject>
	<dc:subject xml:lang="en-US">YOLO</dc:subject>
	<dc:subject xml:lang="en-US">SSD</dc:subject>
	<dc:description xml:lang="en-US">Deep learning’s reliance on abundant data with accurate annotations presents a significant drawback, as developing datasets is often time-consuming and costly for specific problems. To address this drawback, we propose a semi-automatic live-feed image annotation tool called saLFIA. Our case study utilized CCTV data from Indonesia’s toll roads as one of the sources for live-feed images. The primary contribution of saLFIA is a labeling tool designed to generate new datasets from public source images, focusing on vehicle classification using YOLOv3 and SSD algorithms. The evaluation results indicated that SSD achieved higher accuracy with fewer initial images, while YOLOv3 reached maximum accuracy with larger initial datasets, resulting in 8 misdetections out of 380 objects. The saLFIA tool simplifies the annotation process, presenting a labeling tool for creating annotated datasets in a single operation. saLFIA is available at URL https://github.com/gilangmantara/salfia.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2024-10-21</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/20047</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2023.18.2.5</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 18 No. 2 (2024); 143-154</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2023.18.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/20047/6778</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Journal of ICT Research and Applications</dc:rights>
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