Skip to main content

ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1508664

Edge computing for Detection of Ship and Ship Port from remote sensing images using YOLO

Provisionally accepted
Sai Pravallika Marripudi Sai Pravallika Marripudi 1*Vasavi Sanikommu Vasavi Sanikommu 1Harini Reddy Yekkanti Harini Reddy Yekkanti 1Revanth Divi Revanth Divi 1Chandrakanth R Chandrakanth R 2Mahindra P Mahindra P 3
  • 1 Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India
  • 2 Advanced Data Research Institute(ADRIN), Hyderabad, India
  • 3 Department of Space, Advanced Data Research Institute(ADRIN), Hyderabad, India

The final, formatted version of the article will be published soon.

    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.

    Keywords: Ship detection, Ship-port detection, YOLO(You Only Look Once), Edge computing, deep learning, R-CNN, Satellite Imagery

    Received: 09 Oct 2024; Accepted: 02 Jan 2025.

    Copyright: © 2025 Marripudi, Sanikommu, Yekkanti, Divi, R and P. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Sai Pravallika Marripudi, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.