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dc.contributor.authorMINSUNG, SUNG-
dc.contributor.author조현우-
dc.contributor.authorKIM, TAE SIK-
dc.contributor.authorJOE, HAN GIL-
dc.contributor.authorYU, SON CHEOL-
dc.date.available2019-11-06T01:30:03Z-
dc.date.created2019-07-04-
dc.date.issued2019-11-
dc.identifier.issn1530-437X-
dc.identifier.urihttp://oasis.postech.ac.kr/handle/2014.oak/99857-
dc.description.abstractThis paper proposes the detection and removal of crosstalk noise using a convolutional neural network in the images of forward scan sonar. Because crosstalk noise occurs near an underwater object and distorts the shape of the object, underwater object detection is limited. The proposed method can detect crosstalk noise using the neural network and remove crosstalk noise based on the detection result. Thus, the proposed method can be applied to other sonar-image-based algorithms and enhance the reliability of those algorithms. We applied the proposed method to a three-dimensional point cloud generation and generated a more accurate point cloud. We verified the performance of the proposed method by performing multiple indoor and field experiments.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleCrosstalk Removal in Forward Scan Sonar Image Using Deep Learning for Object Detection-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.bibliographicCitationIEEE SENSORS JOURNAL, v.19, no.21, pp.9929 - 9944-
dc.identifier.wosid000492361300039-
dc.citation.endPage9944-
dc.citation.number21-
dc.citation.startPage9929-
dc.citation.titleIEEE SENSORS JOURNAL-
dc.citation.volume19-
dc.contributor.affiliatedAuthorMINSUNG, SUNG-
dc.contributor.affiliatedAuthor조현우-
dc.contributor.affiliatedAuthorKIM, TAE SIK-
dc.contributor.affiliatedAuthorJOE, HAN GIL-
dc.contributor.affiliatedAuthorYU, SON CHEOL-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.type.docTypeArticle-
dc.subject.keywordPluscrosstalk detection-
dc.subject.keywordPlussonar image crosstalk-
dc.subject.keywordPlusunderwater sonar crosstalk-
dc.subject.keywordPlusunderwater object detection.-

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