SHIP DETECTION AND CLASSIFICATION IN CAS500-1 IMAGES BASED ON YOLO MODEL

DC Field Value Language
dc.contributor.author 김태호 -
dc.contributor.author 박영빈 -
dc.contributor.author 장소영 -
dc.contributor.author Yang, Chan Su -
dc.date.accessioned 2023-07-25T01:30:02Z -
dc.date.available 2023-07-25T01:30:02Z -
dc.date.created 2023-07-24 -
dc.date.issued 2023-07-19 -
dc.identifier.issn 0000-0000 -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/44442 -
dc.description.abstract In 2021, compared to the last year, global port container logistic volumes increased by 7% (857 million 20-foot equivalent units (TEU)), while deliveries of newly built container ships increased by 18% (10,929 thousand gross tons) [1]. Hence, the problem instigated by an increased volume of logistics and port congestion can be solved through the efficient operation of the port, and ship detection and volume data processing have become important. Ship monitoring related to port logistics is possible by checking the Auto Identification System (AIS) [2]. -
dc.description.uri 1 -
dc.language English -
dc.publisher IEEE -
dc.relation.isPartOf International Geoscience and Remote Sensing Symposium (IGARSS) -
dc.title SHIP DETECTION AND CLASSIFICATION IN CAS500-1 IMAGES BASED ON YOLO MODEL -
dc.type Conference -
dc.citation.conferenceDate 2023-07-17 -
dc.citation.conferencePlace US -
dc.citation.conferencePlace Pasadena, CA -
dc.citation.endPage 4115 -
dc.citation.startPage 4112 -
dc.citation.title IGARSS 2023 -
dc.contributor.alternativeName 양찬수 -
dc.identifier.bibliographicCitation IGARSS 2023, pp.4112 - 4115 -
dc.identifier.scopusid 2-s2.0-85178382304 -
dc.description.journalClass 1 -
Appears in Collections:
Sea Power Enhancement Research Division > Marine Domain & Security Research Department > 2. Conference Papers
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