Confidence Based Ship Detection Using YOLOv8

Authors

  • Saad Abdullah Bagan UG – CSE (AI&ML) Engineering, Sphoorthy Engineering College, JNTUH, Hyderabad, Telangana, India. Author
  • Gangireddy Mayur UG – CSE (AI&ML) Engineering, Sphoorthy Engineering College, JNTUH, Hyderabad, Telangana, India. Author
  • Chokkalla Vivek Vardhan UG – CSE (AI&ML) Engineering, Sphoorthy Engineering College, JNTUH, Hyderabad, Telangana, India. Author
  • Palvai Lava Kumar UG – CSE (AI&ML) Engineering, Sphoorthy Engineering College, JNTUH, Hyderabad, Telangana, India. Author
  • Surakanti Sphoorthy Reddy Assistant Professor, Department of Computer Science & Engineering (AI&ML), Sphoorthy Engineering College, JNTUH, Hyderabad, Telangana, India. Author
  • Dr. M. Ramesh Professor & Head of the Department, Department of Computer Science & Engineering (AI&ML), Sphoorthy Engineering College, JNTUH, Hyderabad, Telangana, India. Author

DOI:

https://doi.org/10.47392/IRJAEH.2025.0316

Keywords:

Ship Detection, Satellite Imagery, Aerial Imagery, YOLOv8, Real-Time Object Detection, Confidence Threshold Slider, Streamlit Web Interface, Maritime Surveillance, Deep Learning, Lightweight Model Deployment

Abstract

Maritime monitoring and ship detection play a crucial role in ensuring coastal security, managing maritime traffic, and supporting environmental surveillance. This paper presents an efficient and lightweight approach to ship detection using YOLOv8, a state-of-the-art object detection model, applied to both satellite and aerial imagery. The proposed system is designed for real-time inference and enhanced user interaction by integrating a dynamic confidence threshold slider in the web interface. This feature allows users to fine-tune detection sensitivity on-the-fly based on image conditions and detection accuracy preferences. The model is trained on a diverse dataset containing annotated images in YOLO format, ensuring robustness across different imaging conditions and ship sizes. The implementation emphasizes accessibility, allowing deployment on devices without GPU support, and includes a clean, responsive web interface built with Streamlit for intuitive interaction. Experimental results demonstrate reliable detection capabilities with acceptable precision-recall balance for practical applications. The integration of a confidence slider and user-focused design elements marks a significant improvement in usability for non-technical stakeholders, making the system suitable for both research and field deployments.

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Published

2025-05-13

How to Cite

Confidence Based Ship Detection Using YOLOv8. (2025). International Research Journal on Advanced Engineering Hub (IRJAEH), 3(05), 2156-2162. https://doi.org/10.47392/IRJAEH.2025.0316

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