Underwater Object Detection Using YOLO11 Architecture

Srushti Kamble, Riya Khatod, Shreyas Kumbar, Darshan Ghatge, Uday Kulkarni, Sneha Varur

2025

Abstract

Low visibility, noise, and changing object scales all pose substantial hurdles to underwater object detection, limiting the performance of typical detection techniques. This paper introduces YOLO11, an enhanced object detection framework developed to address these issues. The suggested system improves detection accuracy in challenging underwater environments by combining unique strategies such as lightweight attention mechanisms and multi-scale feature fusion. To address the scarcity of labeled datasets, the method employs transfer learning and synthetic data augmentation, ensuring robust generalization across a variety of circumstances. Experimental results show that YOLO11 obtains a precision of 80.4%, recall of 71.1%, and mAP50 of 76.1%, beating earlier models like YOLOv5, YOLOv8, and YOLOv9. Furthermore, YOLO11 has excellent real-time processing capabilities, making it ideal for applications such as environmental surveillance, marine life monitoring, and autonomous underwater vehicles. These developments solidify YOLO11 as a benchmark for underwater object recognition, providing significant insights into its design, training procedures, and performance measures for future study and practical applications.

Paper Citation

in Harvard Style

Kamble S., Khatod R., Kumbar S., Ghatge D., Kulkarni U. and Varur S. (2025). Underwater Object Detection Using YOLO11 Architecture. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 33-40. DOI: 10.5220/0013608200004664

in Bibtex Style

@conference{incoft25,
author={Srushti Kamble and Riya Khatod and Shreyas Kumbar and Darshan Ghatge and Uday Kulkarni and Sneha Varur},
title={Underwater Object Detection Using YOLO11 Architecture},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={33-40},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013608200004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Underwater Object Detection Using YOLO11 Architecture
SN - 978-989-758-763-4
AU - Kamble S.
AU - Khatod R.
AU - Kumbar S.
AU - Ghatge D.
AU - Kulkarni U.
AU - Varur S.
PY - 2025
SP - 33
EP - 40
DO - 10.5220/0013608200004664
PB - SciTePress