Akademik Veri Yönetim Sistemi
9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025, Malatya, Türkiye, 6 - 07 Eylül 2025, (Tam Metin Bildiri)
Detection of ships in Synthetic Aperture Radar (SAR) imagery remains a challenging research problem due to complex background structures, low contrast, and speckle noise. In this study, an effective object detection system is proposed by integrating CLAHE (Contrast Limited Adaptive Histogram Equalization)-based preprocessing with the YOLOv8x deep learning architecture to accurately detect both small and large ships in SAR images. The CLAHE algorithm enhances local contrast and reduces noise and distortions in the images, enabling the network to learn more effectively. Subsequently, the YOLOv8x model is trained on these enhanced images. The experimental evaluation on a custom dataset derived from high-resolution Sentinel-1 SAR imagery reveals that the proposed model outperforms both YOLOv8x and YOLOv8n, achieving 62.42% mAP 50,73.83% precision, and 49.66% recall. Visual outputs further confirm that the proposed model significantly improves the detection of small ships. These findings indicate that the CLAHE+YOLOv8x architecture offers an effective and practical approach for ship detection in SAR imagery.