Edge computing for detection of ship and ship port from remote sensing images using YOLO
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1508664/full |
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| author | Vasavi Sanikommu Sai Pravallika Marripudi Harini Reddy Yekkanti Revanth Divi R. Chandrakanth P. Mahindra |
| author_facet | Vasavi Sanikommu Sai Pravallika Marripudi Harini Reddy Yekkanti Revanth Divi R. Chandrakanth P. Mahindra |
| author_sort | Vasavi Sanikommu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e58dd667597f4f02be23b3f569aa3fdb |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-e58dd667597f4f02be23b3f569aa3fdb2025-02-06T07:10:29ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-02-01810.3389/frai.2025.15086641508664Edge computing for detection of ship and ship port from remote sensing images using YOLOVasavi Sanikommu0Sai Pravallika Marripudi1Harini Reddy Yekkanti2Revanth Divi3R. Chandrakanth4P. Mahindra5Department of Artificial Intelligence and Data Science, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, IndiaDepartment of Artificial Intelligence and Data Science, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, IndiaDepartment of Artificial Intelligence and Data Science, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, IndiaDepartment of Artificial Intelligence and Data Science, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, IndiaDepartment of Space, Advanced Data Research Institute (ADRIN), Hyderabad, IndiaDepartment of Space, Advanced Data Research Institute (ADRIN), Hyderabad, IndiaIn 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.https://www.frontiersin.org/articles/10.3389/frai.2025.1508664/fullship detectionship-port detectionYou Only Look Once (YOLO)edge computingdeep learningR-CNN |
| spellingShingle | Vasavi Sanikommu Sai Pravallika Marripudi Harini Reddy Yekkanti Revanth Divi R. Chandrakanth P. Mahindra Edge computing for detection of ship and ship port from remote sensing images using YOLO Frontiers in Artificial Intelligence ship detection ship-port detection You Only Look Once (YOLO) edge computing deep learning R-CNN |
| title | Edge computing for detection of ship and ship port from remote sensing images using YOLO |
| title_full | Edge computing for detection of ship and ship port from remote sensing images using YOLO |
| title_fullStr | Edge computing for detection of ship and ship port from remote sensing images using YOLO |
| title_full_unstemmed | Edge computing for detection of ship and ship port from remote sensing images using YOLO |
| title_short | Edge computing for detection of ship and ship port from remote sensing images using YOLO |
| title_sort | edge computing for detection of ship and ship port from remote sensing images using yolo |
| topic | ship detection ship-port detection You Only Look Once (YOLO) edge computing deep learning R-CNN |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1508664/full |
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