Paola Barra, Alessia Auriemma Citarella, Giosuè Orefice, M. Castrillón-Santana, A. Ciaramella
{"title":"LOTS: Litter On The Sand dataset for litter segmentation","authors":"Paola Barra, Alessia Auriemma Citarella, Giosuè Orefice, M. Castrillón-Santana, A. Ciaramella","doi":"10.23919/MVA57639.2023.10216220","DOIUrl":null,"url":null,"abstract":"The marine ecosystem is threatened by human waste released into the sea. One of the most challenging marine litter to identify and remove are the small particles settled on the sand which may be ingested by local fauna or cause damage to the marine ecosystem. Those particles are not easy to identify because they get confused with maritime/natural material, natural elements such as shells, stones or others, which can not be classified as \"litter\". In this work we present a dataset of Litter On The Sand (LOTS), with images of clean, dirty and wavy sand from 3 different beaches.","PeriodicalId":338734,"journal":{"name":"2023 18th International Conference on Machine Vision and Applications (MVA)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 18th International Conference on Machine Vision and Applications (MVA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/MVA57639.2023.10216220","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The marine ecosystem is threatened by human waste released into the sea. One of the most challenging marine litter to identify and remove are the small particles settled on the sand which may be ingested by local fauna or cause damage to the marine ecosystem. Those particles are not easy to identify because they get confused with maritime/natural material, natural elements such as shells, stones or others, which can not be classified as "litter". In this work we present a dataset of Litter On The Sand (LOTS), with images of clean, dirty and wavy sand from 3 different beaches.
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