• K Dhyana samaga
• Praneeth P K
• Shreyas Nayak
• Thanush
• Nivin k s, Daya Naik

Ship detection in Synthetic Aperture Radar (SAR) imagery is a critical task with applications in maritime surveillance, environmental monitoring, and security operations. Traditional methods, including Constant False Alarm Rate (CFAR) detectors and hand-crafted feature-based approaches, face significant limitations, such as high false alarm rates, poor generalization, and difficulty in detecting small or camouflaged vessels. This project leverages advanced deep learning techniques to develop a robust and efficient ship detection system tailored for SAR imagery. The system employs sophisticated preprocessing methods for noise reduction and image enhancement, state-of the-art neural network architectures for accurate detection, and hyperparameter optimization to maximize performance. Real-world testing demonstrates significant improvements in detection accuracy and false positive rates, showcasing the system's potential for real-time maritime applications. By addressing the limitations of traditional methods, this project contributes to the field of SAR image analysis and provides a scalable solution for diverse maritime challenges.

Deep Learning-Based Ship Detection in SAR Imagery

"Deep Learning-Based Ship Detection in SAR Imagery", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.703-708, July-2025, Available :http://www.jetir.org/papers/JETIRGX06132.pdf

"Deep Learning-Based Ship Detection in SAR Imagery", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp703-708, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06132.pdf