Underwater Object Detection and Pose Estimation using Deep Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeon, M. | - |
dc.contributor.author | Lee, Y. | - |
dc.contributor.author | Shin, Y.-S. | - |
dc.contributor.author | Jang, H. | - |
dc.contributor.author | Kim, A. | - |
dc.date.accessioned | 2023-12-22T08:01:59Z | - |
dc.date.available | 2023-12-22T08:01:59Z | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 2405-8963 | - |
dc.identifier.uri | https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/8393 | - |
dc.description.abstract | This paper presents an approach for making a dataset using a 3D CAD model for deep learning based underwater object detection and pose estimation. We also introduce a simple pose estimation network for underwater objects. In the experiment, we show that object detection and pose estimation networks trained via our synthetic dataset present a preliminary potential for deep learning based approaches in underwater. Lastly, we show that our synthetic image dataset provides meaningful performance for deep learning models in underwater environments. ? 2019. The Authors. Published by Elsevier Ltd. All rights reserved. | - |
dc.format.extent | 4 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Elsevier B.V. | - |
dc.title | Underwater Object Detection and Pose Estimation using Deep Learning | - |
dc.type | Article | - |
dc.publisher.location | 오스트리아 | - |
dc.identifier.doi | 10.1016/j.ifacol.2019.12.286 | - |
dc.identifier.scopusid | 2-s2.0-85079597652 | - |
dc.identifier.bibliographicCitation | IFAC-PapersOnLine, v.52, no.21, pp 78 - 81 | - |
dc.citation.title | IFAC-PapersOnLine | - |
dc.citation.volume | 52 | - |
dc.citation.number | 21 | - |
dc.citation.startPage | 78 | - |
dc.citation.endPage | 81 | - |
dc.type.docType | Conference Paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | 3D modeling | - |
dc.subject.keywordPlus | Computer aided design | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Marine applications | - |
dc.subject.keywordPlus | Object detection | - |
dc.subject.keywordPlus | Object recognition | - |
dc.subject.keywordPlus | 3D CAD Modeling | - |
dc.subject.keywordPlus | Learning models | - |
dc.subject.keywordPlus | Learning-based approach | - |
dc.subject.keywordPlus | Pose estimation | - |
dc.subject.keywordPlus | Synthetic image dataset | - |
dc.subject.keywordPlus | Underwater environments | - |
dc.subject.keywordPlus | Underwater object detection | - |
dc.subject.keywordPlus | Underwater objects | - |
dc.subject.keywordPlus | Deep learning | - |
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