Deep learning object detection as an assistance system for complex image labeling tasks
Autor(en): | Leimkühler, M. Gravemeier, L.S. Biester, T. Thomas, O. |
Herausgeber: | Bui, T.X. | Stichwörter: | Automation; Efficiency; Object detection; Object recognition, Application scenario; Assistance system; Collaborative settings; Complex image; Fully automated; Human intelligence; Image labeling; Practical use, Deep learning | Erscheinungsdatum: | 2021 | Herausgeber: | IEEE Computer Society | Enthalten in: | Proceedings of the Annual Hawaii International Conference on System Sciences | Band: | 2020-January | Startseite: | 328 | Seitenende: | 337 | Zusammenfassung: | Object detection via deep learning has many promising areas of application. However, robustness and accuracy of fully automated systems are often insufficient for practical use. Integrating results from Artificial Intelligence (AI) and human intelligence in collaborative settings might bridge the gap between efficiency and accuracy. This study proves increased efficiency when supporting human intelligence through AI without negative impact on effectiveness in a fine-grained car scratch image labeling task. Based on the confirmed benefits of AI with human intelligence in the loop approaches, this contribution discusses potential practical application scenarios and envisions the implementation of assistance systems supported by computer vision. © 2021 IEEE Computer Society. All rights reserved. |
Beschreibung: | Conference of 54th Annual Hawaii International Conference on System Sciences, HICSS 2021 ; Conference Date: 4 January 2021 Through 8 January 2021; Conference Code:169537 |
ISBN: | 9780998133140 | ISSN: | 15301605 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108357232&partnerID=40&md5=882dbccab1802b8b635eba3bd3bab04b |
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geprüft am 06.06.2024