{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:00:44Z","timestamp":1780675244525,"version":"3.54.1"},"reference-count":19,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T00:00:00Z","timestamp":1738800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>In marine security and surveillance, accurately identifying ships and ship ports from satellite imagery remains a critical challenge due to the inefficiencies and inaccuracies of conventional approaches. The proposed method uses an enhanced YOLO (You Only Look Once) model, a robust real-time object detection method. The method involves training the YOLO model on an extensive collection of annotated satellite images to detect ships and ship ports accurately. The proposed system delivers a precision of 86% compared to existing methods; this approach is designed to allow for real-time deployment in the context of resource-constrained environments, especially with a Jetson Nano edge device. This deployment will ensure scalability, efficient processing, and reduced reliance on central computing resources, making it especially suitable for maritime settings in which real-time monitoring is vital. The findings of this study, therefore, point out the practical implications of this improved YOLO model for maritime surveillance: offering a scalable and efficient solution to strengthen maritime security.<\/jats:p>","DOI":"10.3389\/frai.2025.1508664","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T07:14:17Z","timestamp":1738826057000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Edge computing for detection of ship and ship port from remote sensing images using YOLO"],"prefix":"10.3389","volume":"8","author":[{"given":"Vasavi","family":"Sanikommu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sai Pravallika","family":"Marripudi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harini Reddy","family":"Yekkanti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Revanth","family":"Divi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Chandrakanth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.","family":"Mahindra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_22","article-title":"\u201cA unified multi-scale deep convolutional neural network for fast object detection,\u201d","author":"Cai","year":"2016","journal-title":"Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV"},{"key":"B2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.04.003","article-title":"Multi-scale object detection in remote sensing imagery with convolutional neural networks","volume":"145","author":"Deng","year":"2018","journal-title":"ISPRS J. 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