Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback
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| Pubblicato in: | Journal of Robotics and Mechatronics vol. 37, no. 2 (Apr 2025), p. 270 |
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| Autore principale: | |
| Altri autori: | , , , |
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Fuji Technology Press Co. Ltd.
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| Accesso online: | Citation/Abstract Full Text - PDF |
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| 022 | |a 0915-3942 | ||
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| 024 | 7 | |a 10.20965/jrm.2025.p0270 |2 doi | |
| 035 | |a 3191586125 | ||
| 045 | 2 | |b d20250401 |b d20250430 | |
| 100 | 1 | |a Kiyokawa Takuya |u Osaka University 1-3 Machikaneyama, Toyonaka, Osaka 560-0043, Japan kiyokawa@sys.es.osaka-u.ac.jp | |
| 245 | 1 | |a Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback | |
| 260 | |b Fuji Technology Press Co. Ltd. |c Apr 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Training deep-learning-based vision systems requires the manual annotation of a significant number of images. Such manual annotation is highly time-consuming and labor-intensive. Although previous studies attempted to eliminate the effort required for annotation, the effort required for image collection was retained. To address this issue, we propose a human-in-the-loop dataset-collection method using a web application. To counterbalance workload and performance by encouraging the collection of multi-view object image datasets enjoyably, thereby amplifying motivation, we propose three types of online visual feedback features to track the progress of the collection status. Our experiments thoroughly investigated the influence of each feature on the collection performance and quality of operation. These results indicate the feasibility of annotation and object detection. | |
| 653 | |a Datasets | ||
| 653 | |a Images | ||
| 653 | |a Annotations | ||
| 653 | |a Object recognition | ||
| 653 | |a Applications programs | ||
| 653 | |a Feedback | ||
| 653 | |a Vision systems | ||
| 653 | |a Collection | ||
| 653 | |a Cameras | ||
| 653 | |a Usability | ||
| 653 | |a Deep learning | ||
| 653 | |a Questionnaires | ||
| 653 | |a Workloads | ||
| 653 | |a Crowdsourcing | ||
| 653 | |a Efficiency | ||
| 653 | |a Robotics | ||
| 700 | 1 | |a Shirakura Naoki |u National Institute of Advanced Industrial Science and Technology 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan | |
| 700 | 1 | |a Katayama Hiroki |u Nara Institute of Science and Technology (NAIST) 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan | |
| 700 | 1 | |a Tomochika Keita |u Nara Institute of Science and Technology (NAIST) 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan | |
| 700 | 1 | |a Takamatsu, Jun |u Nara Institute of Science and Technology (NAIST) 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan | |
| 773 | 0 | |t Journal of Robotics and Mechatronics |g vol. 37, no. 2 (Apr 2025), p. 270 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3191586125/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3191586125/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |