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Journals / Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi / 2026 / Cilt: 42 Sayı: 2

Deep Learning-Based Maritime Vessel Classification for Target Recognition Using Radar and Electro-Optic Imaging

Radar ve Elektro-Optik Görüntüler Kullanılarak Derin Öğrenme Tabanlı Deniz Aracı Sınıflandırması ve Hedef Tanıma

Yalçın Kaplan(Sahil Güvenlik Komutanlığı)0000-0002-3414-1771
Umut Saray(SİVAS CUMHURİYET ÜNİVERSİTESİ, ŞARKIŞLA UYGULAMALI BİLİMLER YÜKSEKOKULU)0000-0003-3339-6876
Pages
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Publication Date
2026-06-02
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results demonstrated classification accuracies ranging from 94% to 99%, highlighting the effectiveness of the proposed approach.

📌 3 Key Takeaways

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Objective & Hypothesis:Critical maritime areas require continuous surveillance and protection, relying on advanced military equipment of strategic importance.
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Key Findings & Data:The deep learning models were trained and evaluated on this dataset to optimize classification performance.
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Conclusion & Impact:Experimental results demonstrated classification accuracies ranging from 94% to 99%, highlighting the effectiveness of the proposed approach.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Derin Öğrenme · Gemi Sınıflandırması · Radar Görüntüleri

Abstract

Critical maritime areas require continuous surveillance and protection, relying on advanced military equipment of strategic importance. The interpretation of images obtained from these systems is essential for situational awareness and decision-making. Moreover, maritime logistics has become a cornerstone of global trade, making the classification and differentiation of ship types crucial for optimizing transportation efficiency, reducing storage costs, and enhancing security. This study focuses on the classification of ships engaged in various maritime missions, with an emphasis on military vessel detection and identification. To achieve high-accuracy ship classification, a deep learning-based approach was adopted. A comprehensive dataset of ship images was constructed using web scraping techniques from publicly available sources. Deep learning was preferred over traditional machine learning techniques due to its ability to extract high-level semantic features and learn complex patterns more effectively. The deep learning models were trained and evaluated on this dataset to optimize classification performance. Experimental results demonstrated classification accuracies ranging from 94% to 99%, highlighting the effectiveness of the proposed approach. This study presents the scientific findings, discusses the implications of the results, and explores potential applications in maritime surveillance and security.

Özet

Kritik deniz alanları, stratejik öneme sahip gelişmiş askerî teçhizatlara dayalı olarak sürekli gözetim ve koruma gerektirmektedir. Bu sistemlerden elde edilen görüntülerin yorumlanması, durumsal farkındalık ve karar verme süreçleri açısından büyük önem taşımaktadır. Bunun yanı sıra, deniz lojistiği küresel ticaretin temel taşlarından biri hâline gelmiş olup, gemi türlerinin sınıflandırılması ve birbirinden ayırt edilmesi; taşımacılık verimliliğinin artırılması, depolama maliyetlerinin azaltılması ve güvenliğin güçlendirilmesi açısından kritik bir rol oynamaktadır. Bu çalışma, farklı deniz görevlerinde faaliyet gösteren gemilerin sınıflandırılmasına odaklanmakta olup, özellikle askerî gemilerin tespiti ve tanımlanmasını ön plana çıkarmaktadır. Yüksek doğrulukta gemi sınıflandırması elde edebilmek amacıyla derin öğrenme tabanlı bir yaklaşım benimsenmiştir. Kamuya açık kaynaklardan web kazıma (web scraping) teknikleri kullanılarak kapsamlı bir gemi görüntü veri kümesi oluşturulmuştur. Derin öğrenme, yüksek seviyeli anlamsal özellikleri çıkarabilme ve karmaşık örüntüleri daha etkin şekilde öğrenebilme yeteneği nedeniyle geleneksel makine öğrenmesi yöntemlerine tercih edilmiştir. Derin öğrenme modelleri, sınıflandırma performansını optimize etmek amacıyla bu veri kümesi üzerinde eğitilmiş ve test edilmiştir. Deneysel sonuçlar, %94 ile %99 arasında değişen sınıflandırma doğrulukları elde edildiğini göstermiş olup, önerilen yaklaşımın etkinliğini açıkça ortaya koymaktadır. Bu çalışma, elde edilen bilimsel bulguları sunmakta, sonuçların etkilerini tartışmakta ve deniz gözetleme ile güvenlik alanındaki potansiyel uygulamaları irdelemektedir.

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Cite This Article

Kaplan, Y., Saray, U. (2026). Deep Learning-Based Maritime Vessel Classification for Target Recognition Using Radar and Electro-Optic Imaging. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, Cilt: 42 Sayı: 2. https://doi.org/10.65520/erciyesfen.1887033

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