분류학습모델과 CNN 모델을 이용한 해상물체 식별 성능 평가 방법 연구
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 서동민 | - |
dc.contributor.author | 오상우 | - |
dc.contributor.author | 박세길 | - |
dc.date.accessioned | 2024-01-10T12:31:48Z | - |
dc.date.available | 2024-01-10T12:31:48Z | - |
dc.date.issued | 20231215 | - |
dc.identifier.uri | https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/10204 | - |
dc.description.abstract | We conducted a study to identify small objects floating at sea using machine learning technology based on hyperspectral image data. Classification learning models and CNN models were used to identify maritime objects, and the accuracy of identification of maritime objects was quantitatively analyzed. In addition, the performance between each model was compared by calculating detailed identification accuracy according to the classified objects. | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.title | 분류학습모델과 CNN 모델을 이용한 해상물체 식별 성능 평가 방법 연구 | - |
dc.title.alternative | Study on maritime object identification performance evaluation using classification learning models and CNN models | - |
dc.type | Conference | - |
dc.citation.startPage | 54 | - |
dc.citation.endPage | 55 | - |
dc.citation.conferenceName | 2023년도 대한전자공학회 학술심포지움 | - |
dc.citation.conferencePlace | 대한민국 | - |
dc.citation.conferencePlace | 대전 충남대학교 | - |
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