saLFIA: Semi-automatic Live Feeds Image Annotation Tool for Vehicle Classification Dataset
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 u...
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| Format: | Article |
| Language: | English |
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ITB Journal Publisher
2024-10-01
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| Series: | Journal of ICT Research and Applications |
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| Online Access: | https://journals.itb.ac.id/index.php/jictra/article/view/20047 |
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| author | Umi Chasanah Gilang Putra Sahid Bismantoko Sofwan Hidayat Tri Widodo Mohammad Rosyidi |
| author_facet | Umi Chasanah Gilang Putra Sahid Bismantoko Sofwan Hidayat Tri Widodo Mohammad Rosyidi |
| author_sort | Umi Chasanah |
| collection | DOAJ |
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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.
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| format | Article |
| id | doaj-art-5db341e16a104ff88fcd8cf0c5e89e90 |
| institution | DOAJ |
| issn | 2337-5787 2338-5499 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | ITB Journal Publisher |
| record_format | Article |
| series | Journal of ICT Research and Applications |
| spelling | doaj-art-5db341e16a104ff88fcd8cf0c5e89e902025-08-20T03:22:19ZengITB Journal PublisherJournal of ICT Research and Applications2337-57872338-54992024-10-01182saLFIA: Semi-automatic Live Feeds Image Annotation Tool for Vehicle Classification DatasetUmi Chasanah0Gilang Putra1Sahid Bismantoko2Sofwan Hidayat3Tri Widodo4Mohammad Rosyidi5Research Center for Artificial Intelligence and Cyber Security, National Research and Innovation Agency, Jalan Cisitu Sangkuriang, Bandung 40135Research Center for Artificial Intelligence and Cyber Security, National Research and Innovation Agency, Jalan Cisitu Sangkuriang, Bandung 40135Research Center for Computing, National Research and Innovation Agency, Jalan Raya Jakarta - Bogor KM 46 Cibinong 16911Research Center for Transportation Technology, National Research and Innovation Agency, Kawasan PUSPIPTEK, Tangerang Selatan 15314Research Center for Transportation Technology, National Research and Innovation Agency, Kawasan PUSPIPTEK, Tangerang Selatan 15314Research Center for Computing, National Research and Innovation Agency, Jalan Raya Jakarta - Bogor KM 46 Cibinong 16911 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. https://journals.itb.ac.id/index.php/jictra/article/view/20047annotation toolCCTVdatasetvehicle classificationYOLOSSD |
| spellingShingle | Umi Chasanah Gilang Putra Sahid Bismantoko Sofwan Hidayat Tri Widodo Mohammad Rosyidi saLFIA: Semi-automatic Live Feeds Image Annotation Tool for Vehicle Classification Dataset Journal of ICT Research and Applications annotation tool CCTV dataset vehicle classification YOLO SSD |
| title | saLFIA: Semi-automatic Live Feeds Image Annotation Tool for Vehicle Classification Dataset |
| title_full | saLFIA: Semi-automatic Live Feeds Image Annotation Tool for Vehicle Classification Dataset |
| title_fullStr | saLFIA: Semi-automatic Live Feeds Image Annotation Tool for Vehicle Classification Dataset |
| title_full_unstemmed | saLFIA: Semi-automatic Live Feeds Image Annotation Tool for Vehicle Classification Dataset |
| title_short | saLFIA: Semi-automatic Live Feeds Image Annotation Tool for Vehicle Classification Dataset |
| title_sort | salfia semi automatic live feeds image annotation tool for vehicle classification dataset |
| topic | annotation tool CCTV dataset vehicle classification YOLO SSD |
| url | https://journals.itb.ac.id/index.php/jictra/article/view/20047 |
| work_keys_str_mv | AT umichasanah salfiasemiautomaticlivefeedsimageannotationtoolforvehicleclassificationdataset AT gilangputra salfiasemiautomaticlivefeedsimageannotationtoolforvehicleclassificationdataset AT sahidbismantoko salfiasemiautomaticlivefeedsimageannotationtoolforvehicleclassificationdataset AT sofwanhidayat salfiasemiautomaticlivefeedsimageannotationtoolforvehicleclassificationdataset AT triwidodo salfiasemiautomaticlivefeedsimageannotationtoolforvehicleclassificationdataset AT mohammadrosyidi salfiasemiautomaticlivefeedsimageannotationtoolforvehicleclassificationdataset |