{"refrec":{"BRefID":359155,"RR":"Hipolito, J.C.; Sarraga Alon, A.; Amorado, R.V.; Fernando, M.G.Z.; De Chavez, P.I.C. (2021). Detection of underwater marine plastic debris using an augmented low sample size dataset for machine vision system: A deep transfer learning approach, in: 19th IEEE Student Conference on Research and Development (SCOReD 2021). pp. 82-86. https://dx.doi.org/10.1109/scored53546.2021.9652703","BEntID":356870,"PublicFlag":1,"CheckedFlag":0,"wosflag":0,"vabbflag":0,"RefStringPartII":", in: 19th IEEE Student Conference on Research and Development (SCOReD 2021). pp. 82-86. https://dx.doi.org/10.1109/scored53546.2021.9652703","DocTypID":17,"DocType":"Book chapters","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Hipolito, J.C.; Sarraga Alon, A.; Amorado, R.V.; Fernando, M.G.Z.; De Chavez, P.I.C.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Hipolito, J.C. et al.","Englishabstract":"Waste in aquatic environments devastates aquatic habitats and offers a tall environmental and economical risk. Machine Vision might play a role in resolving this issue by detecting and finally eliminating debris. Using an augmented low sample size from a publicly available collection of underwater plastic waste, this research employed a YOLOv3 deep-learning system to visually recognize debris in realistic underwater environments. The detection model has a training and validation accuracy of 98.026 % and 94.582 %, respectively, according to the study's findings, with an mAP value of 98.15%. With its effectiveness in detecting underwater plastic waste, the recommended model is suitable for a variety of machine vision systems. The system has a 100% testing accuracy, with detection per frame accuracy ranging from 60.59% to 98.89%.","AbstractOtherLang":null,"BibLvlCode":"AM","StandardTitle":"Detection of underwater marine plastic debris using an augmented low sample size dataset for machine vision system: A deep transfer learning approach","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2024-12-10 01:33:17.368041","timezone_type":1,"timezone":"+01:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"object detection, deep learning, transfer learning, marine plastic waste detection, yolov3","OtherDescriptors":null,"Notes":null,"AnaPub":2021,"MonPub":null,"DateUpdate":"2022-11-10","DateCreate":"2022-11-10","SecASFANote":null,"ConfID":null,"PeerRev":0,"VlizCoreFlag":1,"WoScode":"WOS:000786193400015","VABBcode":null,"OpenAcc":0,"DOI":"10.1109/scored53546.2021.9652703"},"refs":null,"anarec":{"AnaID":359155,"PubliDate":2021,"Pagination":"82-86","XtraPublOfAnaID":null,"ISBN":"978-1-6654-0193-7","Volume":null,"Issue":null,"BRefMon":359153,"BRefMonRR":"(2021). 19th IEEE Student Conference on Research and Development (SCOReD 2021). 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