{"refrec":{"BRefID":363751,"RR":"<b>Zheng, J.; Cao, J.; Yuan, K.; Liu, Y.</b> (2023). A small fishing vessel recognition method using transfer learning based on laser sensors. <i>NPG Scientific Reports 13(1)</i>: 5931. <a href=\"https://dx.doi.org/10.1038/s41598-023-31319-y\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-023-31319-y</a>","BEntID":361471,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>NPG Scientific Reports 13(1)</i>: 5931. <a href=\"https://dx.doi.org/10.1038/s41598-023-31319-y\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-023-31319-y</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Zheng, J.; Cao, J.; Yuan, K.; Liu, Y.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Zheng, J. <i>et al.</i>","Englishabstract":"The management of small vessels has always been key to maritime administration. This paper presents a novel method for recognizing small fishing vessels based on laser sensors. Using four types of small fishing vessels as targets, a recognition method for small fishing vessels based on Markov transition field (MTF) time-series images and VGG-16 transfer learning is proposed. In contrast to conventional methods, this study uses polynomial fitting to obtain the contours of a fishing vessel and transforms one-dimensional vessel contours into two-dimensional time-series images using the MTF coding method. The VGG-16 model is used for the recognition process, and migration learning is applied to improve the results. The UCR time-series public dataset is used as a transfer learning dataset for the MTF time-series image encoding. The experiment demonstrates that the proposed method exhibits higher accuracy and performance than 1D-CNN and other general neural network models, and the highest accuracy rate is 98.92%.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"A small fishing vessel recognition method using transfer learning based on laser sensors","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-06-13 01:31:59.981950","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":null,"OtherDescriptors":null,"Notes":null,"AnaPub":2023,"MonPub":null,"DateUpdate":"2023-04-18","DateCreate":"2023-04-18","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:001034742000008","VABBcode":null,"OpenAcc":1,"DOI":"10.1038/s41598-023-31319-y"},"refs":null,"anarec":{"AnaID":363751,"PubliDate":2023,"Pagination":"5931","XtraPublOfAnaID":null,"ISBN":null,"Volume":"13","Issue":"1","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":208093,"SerRR":"Scientific Reports (Nature Publishing Group). 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