A Comparative Study of Real-Time, Deep-Learning-Based Object Detection Techniques for Underwater Litter Detection

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.