{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:41:17Z","timestamp":1780634477841,"version":"3.54.1"},"reference-count":57,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T00:00:00Z","timestamp":1732406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad Polit\u00e9cnica de Cartagena (UPCT), Cartagena, Spain"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The evolution of maritime surveillance is significantly marked by the incorporation of Artificial Intelligence and machine learning into Unmanned Surface Vehicles (USVs). This paper presents an AI approach for detecting and tracking unmanned surface vehicles, specifically leveraging an enhanced version of YOLOv8, fine-tuned for maritime surveillance needs. Deployed on the NVIDIA Jetson TX2 platform, the system features an innovative architecture and perception module optimized for real-time operations and energy efficiency. Demonstrating superior detection accuracy with a mean Average Precision (mAP) of 0.99 and achieving an operational speed of 17.99 FPS, all while maintaining energy consumption at just 5.61 joules. The remarkable balance between accuracy, processing speed, and energy efficiency underscores the potential of this system to significantly advance maritime safety, security, and environmental monitoring.<\/jats:p>","DOI":"10.3390\/jimaging10120303","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T06:25:19Z","timestamp":1732515919000},"page":"303","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Enhanced YOLOv8 Ship Detection Empower Unmanned Surface Vehicles for Advanced Maritime Surveillance"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1521-208X","authenticated-orcid":false,"given":"Abdelilah","family":"Haijoub","sequence":"first","affiliation":[{"name":"Engineering Sciences Laboratory, National School of Applied Sciences of Kenitra, Ibn Tofail University, Kenitra 14000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anas","family":"Hatim","sequence":"additional","affiliation":[{"name":"Laboratory of Research on Sustainable and Innovative Technologies (LaRTID), National School of Applied Sciences of Marrakech, Cadi Ayyad University, Marrakech 40000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1485-4758","authenticated-orcid":false,"given":"Antonio","family":"Guerrero-Gonzalez","sequence":"additional","affiliation":[{"name":"Department of Automation, Electrical Engineering and Electronic Technology, Universidad Polit\u00e9cnica de Cartagena, 30202 Cartagena, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mounir","family":"Arioua","sequence":"additional","affiliation":[{"name":"Engineering Sciences Laboratory, National School of Applied Sciences of Kenitra, Ibn Tofail University, Kenitra 14000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khalid","family":"Chougdali","sequence":"additional","affiliation":[{"name":"Engineering Sciences Laboratory, National School of Applied Sciences of Kenitra, Ibn Tofail University, Kenitra 14000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Molina-Molina, J.C., Salhaoui, M., Guerrero-Gonz\u00e1lez, A., and Arioua, M. 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