카메라 기반 객체의 위치인식을 위한 왜곡제거 및 오검출 필터링 기법
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 진실 | - |
dc.contributor.author | 송지민 | - |
dc.contributor.author | 최지호 | - |
dc.contributor.author | 진용식 | - |
dc.contributor.author | 정재진 | - |
dc.contributor.author | 이상준 | - |
dc.date.accessioned | 2024-03-13T02:00:27Z | - |
dc.date.available | 2024-03-13T02:00:27Z | - |
dc.date.issued | 2024-02 | - |
dc.identifier.issn | 1975-5066 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28530 | - |
dc.description.abstract | Robotic arms have been widely utilized in various labor-intensive industries such as manufacturing, agriculture, and food services, contributing to increasing productivity. In the development of industrial robotic arms, camera sensors have many advantages due to their cost-effectiveness and small sizes. However, estimating object positions is a challenging problem, and it critically affects to the robustness of object manipulation functions. This paper proposes a method for estimating the 3D positions of objects, and it is applied to a pick-and-place task. A deep learning model is utilized to detect 2D bounding boxes in the image plane, and the pinhole camera model is employed to compute the object positions. To improve the robustness of measuring the 3D positions of objects, we analyze the effect of lens distortion and introduce a false positive filtering process. Experiments were conducted on a real-world scenario for moving medicine bottles by using a camera-based manipulator. Experimental results demonstrated that the distortion removal and false positive filtering are effective to improve the position estimation precision and the manipulation success rate. | - |
dc.format.extent | 7 | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.publisher | 대한임베디드공학회 | - |
dc.title | 카메라 기반 객체의 위치인식을 위한 왜곡제거 및 오검출 필터링 기법 | - |
dc.title.alternative | Distortion Removal and False Positive Filtering for Camera-based Object Position Estimation | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.doi | 10.14372/IEMEK.2024.19.1.1 | - |
dc.identifier.bibliographicCitation | 대한임베디드공학회논문지, v.19, no.1, pp 1 - 7 | - |
dc.citation.title | 대한임베디드공학회논문지 | - |
dc.citation.volume | 19 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 7 | - |
dc.identifier.kciid | ART003055231 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Computer vision | - |
dc.subject.keywordAuthor | Manipulator robot | - |
dc.subject.keywordAuthor | Distance estimation | - |
dc.subject.keywordAuthor | Object detection | - |
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