2026-06-16原文2 min readUpdated: 2026-06-16

AI for Maritime Security: Comparative Evaluation of CNN and Vision Transformer Architectures for Maritime Object Detection

This study enhances maritime security using AI and computer vision, comparing six deep learning architectures on 6,468 images. The Vision Transformer achieved 100% accuracy with the lowest error rates and fastest processing, demonstrating AI's potential for surveillance, border protection, and autonomous navigation.

SourcearXiv Computer VisionAuthor: Ismet Gocer, Zakirul Bhuiayn, Shakeel Ahmad, Raza Hasan

[2606.14720] AI for Maritime Security: Comparative Evaluation of CNN and Vision Transformer Architectures for Maritime Object Detection

[Submitted on 28 May 2026]

Title:AI for Maritime Security: Comparative Evaluation of CNN and Vision Transformer Architectures for Maritime Object Detection

View a PDF of the paper titled AI for Maritime Security: Comparative Evaluation of CNN and Vision Transformer Architectures for Maritime Object Detection, by Ismet Gocer and 3 other authors

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Abstract:This study aims to enhance maritime security by using advanced Artificial Intelligence (AI) and Computer Vision (CV) techniques. For this purpose, it was designed and assessed intelligent object detection systems that can detect the presence of ships on the sea surface under different real-time environments. To achieve this goal, a maritime image dataset with 6,468 images was used, covering different weather conditions like cloudy, foggy, rainy, and sunny environments. Six deep learning architectures were evaluated, including a base Convolutional Neural Network (CNN) model, four transfer learning models (Xception, VGG16, MobileNetV2, and EfficientNetV2L), and a Vision Transformer (ViT) model. The models were compared using multiple performance indicators, including accuracy, Type I and Type II errors, model size, and video processing time. The results show that model performance varies depending on computational constraints and deployment conditions. While lightweight architectures are suitable for resource-limited devices, the ViT achieved the best overall performance, reaching 100% accuracy with the lowest error rates and the fastest video processing time. The findings highlight the potential of AI-driven computer vision systems for maritime surveillance, border protection, and autonomous navigation.

Comments: 24 Pages

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2606.14720 [cs.CV]

(or arXiv:2606.14720v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.14720

arXiv-issued DOI via DataCite

Submission history

From: Ismet Gocer [view email] [v1] Thu, 28 May 2026 11:14:54 UTC (1,502 KB)

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