Reference
K. ter Burg, A. Ilioudi,
E. P. M.
Troquay,
A. M. Vincent, M. Guo, and B. De
Schutter, "A comparative study of real-time, deep-learning-based object
detection techniques for underwater litter detection,"
Proceedings of the OCEANS 2025 Great Lakes, Chicago,
lllinois, 8 pp., Sept.-Oct. 2025.
Abstract
Marine litter pollution is a major environmental threat due to the widespread
presence of plastics and their detrimental impact on marine life and human
health. There is a need for autonomous systems with computer vision to help
clean the oceans. This study compares the latest state-of-the-art You Only Look
Once (YOLO) models YOLOv9 - YOLOv12 in an underwater object detection setting
in terms of accuracy, computational speed, and architecture complexity. We
specifically focus on the smallest versions of these architectures, due to the
real-time constraints of the setting. Multiple underwater datasets are combined
to obtain a wide representation of underwater conditions and marine objects.
The findings provide valuable insights into selecting and optimizing object
detection architectures for underwater litter detection, contributing to
monitoring marine ecosystems and addressing marine pollution. This work can be
used as a building ground for further improving underwater object detection
systems.
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BibTeX
@inproceedings{TerIli:25-021,
author = {ter Burg, Kaya and Ilioudi, Athina and Troquay, Eline P. M. and
Vincent, Amala Mary and Guo, Meichen and De Schutter, Bart},
title = {A Comparative Study of Real-Time, Deep-Learning-Based Object
Detection Techniques for Underwater Litter Detection},
booktitle = {Proceedings of the OCEANS 2025 Great Lakes},
address = {Chicago, lllinois},
month = sep # {--} # oct,
year = {2025}
}