Underwater Detection of Ancient Pottery Sherds Using Deep Learning

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We trained multiple iterations of the YOLOv8 model using a custom dataset comprised of underwater videos taken during diving expeditions at the wreck site. The primary goal of this research is to integrate the resulting object detection system into a remotely operated vehicle (ROV) for automated pottery shard recognition, thereby aiding archaeological excavations. The paper elaborates on the model's development methodology and presents comprehensive experimental and evaluative results. These findings underscore the model's potential to significantly enhance the ef iciency and accuracy of underwater archaeological exploration and analysis."> <meta name="keywords" content=" Ancient pottery shreds detection, underwater archaeological excavations, machine learning, object detection, remotely operated vehicle (ROV), underwater shipwrecks, YOLOv8 model , Proceedings, Computer Science, Technology, open access proceedings"/>   <meta name="dc.title" content="Underwater Detection of Ancient Pottery Sherds Using Deep Learning "> <meta name="citation_authors" content="Konstantinos Paraskevas"> <meta name="citation_authors" content=" Ioannis Mariolis"> <meta name="citation_authors" content=" Georgios Giouvanis"> <meta name="citation_authors" content=" Georgia Peleka"> <meta name="citation_authors" content=" Georgios Zampokas"> <meta name="citation_authors" content=" Dimitrios Tzovaras"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="International Journal on Cybernetics & Informatics (IJCI) Vol.12, No.6"> <meta name="dc.date" content="2023/10/07"> <meta name="dc.identifier" content="10.5121/ijci.2023.120601"> <meta name="dc.publisher" content="AIRCC Publishing Corporation"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf"> <meta name="dc.language" content="en"> <meta name="dc.description" content=" This paper outlines the creation of a machine learning model designed to identify ancient pottery fragments near a submerged shipwreck of Modi Island, Greece. We trained multiple iterations of the YOLOv8 model using a custom dataset comprised of underwater videos taken during diving expeditions at the wreck site. The primary goal of this research is to integrate the resulting object detection system into a remotely operated vehicle (ROV) for automated pottery shard recognition, thereby aiding archaeological excavations. The paper elaborates on the model's development methodology and presents comprehensive experimental and evaluative results. These findings underscore the model's potential to significantly enhance the ef iciency and accuracy of underwater archaeological exploration and analysis."/> <meta name="dc.subject" content="Ancient pottery shreds detection"> <meta name="dc.subject" content="underwater archaeological excavations"> <meta name="dc.subject" content="machine learning"> <meta name="dc.subject" content="object detection"> <meta name="dc.subject" content="remotely operated vehicle (ROV)"> <meta name="dc.subject" content="underwater shipwrecks"> <meta name="dc.subject" content="YOLOv8 model"> <meta name="dc.subject" content="Proceedings"> <meta name="dc.subject" content="Computer Science"> <meta name="dc.subject" content="Technology"> <meta name="dc.subject" content="open access proceedings">   <meta name="prism.publicationName" content="International Journal on Cybernetics & Informatics (IJCI) "> <meta name="prism.publicationDate" content="2023/10/07"> <meta name="prism.volume" content="12"> <meta name="prism.number" content="06"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="1">   <meta name="citation_journal_title" content="International Journal on Cybernetics & Informatics (IJCI)"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_authors" content="Konstantinos Paraskevas, Ioannis Mariolis, Georgios Giouvanis, Georgia Peleka, Georgios Zampokas and Dimitrios Tzovaras "> <meta name="citation_title" content="Underwater Detection of Ancient Pottery Sherds Using Deep Learning"> <meta name="citation_online_date" content="2023/10/07"> <meta name="citation_issue" content="12"> <meta name="citation_firstpage" content="1"> <meta name="citation_authors" content="Konstantinos Paraskevas"> <meta name="citation_authors" content=" Ioannis Mariolis"> <meta name="citation_authors" content=" Georgios Giouvanis"> <meta name="citation_authors" content=" Georgia Peleka"> <meta name="citation_authors" content=" Georgios Zampokas"> <meta name="citation_authors" content=" Dimitrios Tzovaras"> <meta name="citation_doi" content="10.5121/ijci.2023.120601"> <meta name="citation_abstract_html_url" content="https://ijcionline.com/abstract/12623ijci01"> <meta name="citation_pdf_url" content="https://ijcionline.com/paper/12/12623ijci01.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://ijcionline.com/abstract/12623ijci01"> <meta property="og:title" content="Underwater Detection of Ancient Pottery Sherds Using Deep Learning"> <meta property="og:description" content=" This paper outlines the creation of a machine learning model designed to identify ancient pottery fragments near a submerged shipwreck of Modi Island, Greece. We trained multiple iterations of the YOLOv8 model using a custom dataset comprised of underwater videos taken during diving expeditions at the wreck site. The primary goal of this research is to integrate the resulting object detection system into a remotely operated vehicle (ROV) for automated pottery shard recognition, thereby aiding archaeological excavations. The paper elaborates on the model's development methodology and presents comprehensive experimental and evaluative results. These findings underscore the model's potential to significantly enhance the ef iciency and accuracy of underwater archaeological exploration and analysis."/>   <meta name="twitter:card" content="Proceedings" /> <meta name="twitter:site" content="AIRCC" /> <meta name="twitter:title" content="Underwater Detection of Ancient Pottery Sherds Using Deep Learning" /> <meta name="twitter:description" content="This paper outlines the creation of a machine learning model designed to identify ancient pottery fragments near a submerged shipwreck of Modi Island, Greece. We trained multiple iterations of the YOLOv8 model using a custom dataset comprised of underwater videos taken during diving expeditions at the wreck site. The primary goal of this research is to integrate the resulting object detection system into a remotely operated vehicle (ROV) for automated pottery shard recognition, thereby aiding archaeological excavations. The paper elaborates on the model's development methodology and presents comprehensive experimental and evaluative results. 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We trained multiple iterations of the YOLOv8 model using a custom dataset comprised of underwater videos taken during diving expeditions at the wreck site. The primary goal of this research is to integrate the resulting object detection system into a remotely operated vehicle (ROV) for automated pottery shard recognition, thereby aiding archaeological excavations. The paper elaborates on the model's development methodology and presents comprehensive experimental and evaluative results. These findings underscore the model's potential to significantly enhance the ef iciency and accuracy of underwater archaeological exploration and analysis. </p> </div> </div> <div class="card"> <h5 id="about" class="brown-text text-darken-2 text-center" style="padding-bottom:0px">Keywords</h5>  <div class="card-content"> <p class="left-text" style="text-align:justify"> Ancient pottery shreds detection, underwater archaeological excavations, machine learning, object detection, remotely operated vehicle (ROV), underwater shipwrecks, YOLOv8 model. </p> </div> </div> <div class="card-content"> <a href="/paper/12/12623ijci01.pdf" target="_blank" class="btn btn-small lighten-2 cyan lig">Full Text</a>  <a href="/volume12" target="_blank" class="btn btn-small lighten-2 cyan lig">Volume 12,Number 06</a> <a href="https://www.youtube.com/playlist?list=PL1HkUyqULCxznHOi4QhmWInZ4Gb_YrthM" target="_blank" class="btn btn-small lighten-2 cyan lig">Presentation</a> </div> </div>  <div id="side-bar" class="col s12 m3"> <div id="section-main"> <br> <br> <div class="card side cyan lighten-2"> <div class="card-content"> <ul> <li class="ax waves-effect waves-light"> <a class="white-text" href="/editorial" > <i class="material-icons left">account_circle</i>Editorial Board</a> <br> </li> <br> <br> <div class="divider"></div> <br> <li class="ax waves-effect waves-light"> <a class="white-text" href="/mostcitedarticels" > <i class="material-icons left">book</i>Most Cited Articels </a> <br> </li> <br> <br> <div class="divider"></div> <br> <li class="ax waves-effect waves-light"> <a class="white-text" href="/indexing" > <i class="material-icons left">list</i>Indexing </a> <br> </li> <br> <br> <div class="divider"></div> <br> <li class="ax waves-effect waves-light"> <a class="white-text" href="/faq" > <i class="material-icons left">help</i>FAQ </a> <br> </li> <br> <br> <div class="divider"></div> <br> </div> </div> </div> </div> </div> </div> </div> </section> <br> <br> <br> <br> <div id="txtcnt"></div>  <footer class="page-footer cyan lighten-3"> <div class="nav-wrapper"> <div class="container"> <ul> <li> <a target="_blank" href="http://airccse.org/"> <img src="/img/since2008.png" alt="since2008"></a> </li> </ul> <h6> Free Open Access Conference Proceedings <br> Computer Science & Engineering - Information Technology - Information Systems</h6> </div> <div class="footer-m col m3 s12 offset-m1"> </div> <div class="social col m3 offset-m1 s12"> </div> </div> </div> <div class="col s12 m10 offset-m1"> <div class="grey darken-3 center-align"> <small class="white-text">Designed and Developed by NNN Team</small> </div> </div> </footer> </body>  <script type="text/javascript" src="https://code.jquery.com/jquery-3.2.1.min.js"></script> <script type="text/javascript" src="/js/materialize.min.js"></script> <script src="/js/search.js"></script> <script src="/js/scrolltop.js"></script> <script src="/js/popup.js"></script> <script src="/js/main.jquery.js"></script> </html>

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Underwater Detection of Ancient Pottery Sherds Using Deep Learning