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Journals / Beykent Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2026 / Cilt: 19 Sayı: 1

The Effect of SRCNN-Based Preprocessing on YOLOv8 Performance in Ship Detection with SAR Images

SAR Görüntüleri ile Gemi Tespitinde SRCNN Tabanlı Ön İşlemenin YOLOv8 Performansına Etkisi

Ömer Kanlıkuyu(YILDIZ TEKNİK ÜNİVERSİTESİ)0009-0006-3118-1010
Bülent Bolat(YILDIZ TEKNİK ÜNİVERSİTESİ, ELEKTRİK-ELEKTRONİK FAKÜLTESİ, ELEKTRONİK VE HABERLEŞME MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-2468-8618
Pages
1–13
Publication Date
2026-06-29
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AI Summary & Key Takeaways

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

The findings indicate that models trained with preprocessed data provide improvements in precision, recall, mAP@0,50 and mAP@0,50:0,95 metrics, and generate more stable detections especially in scenes with complex backgrounds.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study investigates the effect of SRCNN-based preprocessing on the performance of the YOLOv8 model used for ship detection in Synthetic Aperture Radar (SAR) images.
📊
Key Findings & Data:Comparative experiments were conducted on raw and enhanced images by using the open-source SSDD dataset.
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Conclusion & Impact:The findings indicate that models trained with preprocessed data provide improvements in precision, recall, mAP@0,50 and mAP@0,50:0,95 metrics, and generate more stable detections especially in scenes with complex backgrounds.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Sentetik Açıklıklı Radar (SAR) · Gemi Tespiti · YOLOv8

Abstract

This study investigates the effect of SRCNN-based preprocessing on the performance of the YOLOv8 model used for ship detection in Synthetic Aperture Radar (SAR) images. Comparative experiments were conducted on raw and enhanced images by using the open-source SSDD dataset. The findings indicate that models trained with preprocessed data provide improvements in precision, recall, mAP@0,50 and mAP@0,50:0,95 metrics, and generate more stable detections especially in scenes with complex backgrounds.

Özet

Bu çalışma, Sentetik Açıklıklı Radar (SAR) görüntülerinde gemi tespiti için kullanılan YOLOv8 modelinin performansı üzerinde SRCNN tabanlı ön işlemenin etkisini incelemektedir. Açık kaynaklı SSDD veri seti kullanılarak ham ve iyileştirilmiş görüntüler üzerinde karşılaştırmalı deneyler yürütülmüştür. Elde edilen bulgular, ön işleme uygulanan verilerle eğitilen modellerin kesinlik, duyarlılık ve mAP@0,50 ve mAP@0,50:0,95 ölçütlerinde iyileşme sağladığını ve özellikle yoğun arka plan içeren sahnelerde daha kararlı tespitler ürettiğini göstermektedir.

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Cite This Article

Kanlıkuyu, Ö., Bolat, B. (2026). The Effect of SRCNN-Based Preprocessing on YOLOv8 Performance in Ship Detection with SAR Images. Beykent Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 19 Sayı: 1, 1–13. https://doi.org/10.20854/bujse.1919256

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