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. 2025 Feb 6:8:1508664.
doi: 10.3389/frai.2025.1508664. eCollection 2025.

Edge computing for detection of ship and ship port from remote sensing images using YOLO

Vasavi Sanikommu  1 , Sai Pravallika Marripudi  1 , Harini Reddy Yekkanti  1 , Revanth Divi  1 , R Chandrakanth  2 , P Mahindra  2
Affiliations

Edge computing for detection of ship and ship port from remote sensing images using YOLO

Vasavi Sanikommu et al. Front Artif Intell. .

Abstract

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: R-CNN; You Only Look Once (YOLO); deep learning; edge computing; satellite imagery; ship detection; ship-port detection.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Proposed model architecture.
Figure 2
Figure 2
Enhanced YOLO architecture.
Figure 3
Figure 3
R-CNN model architecture.
Figure 4
Figure 4
Sample dataset images.
Figure 5
Figure 5
Epochs vs. loss.
Figure 6
Figure 6
Accuracy vs. epochs.
Figure 7
Figure 7
Training and validation metrics.
Figure 8
Figure 8
Result of ship port detection.
See this image and copyright information in PMC

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