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A Comparison of Self-Supervised and Supervised Deep Learning Approaches in Floating Marine Litter and Other Types of Sea-Surface Anomalies Detection
Summary
Comparing supervised (YOLOv11) and self-supervised (MoCo + CatBoost) machine learning on Arctic sea-surface imagery found that self-supervised learning outperformed object detection for identifying visually ambiguous floating marine litter (40% vs. 10% F1-score), while object detection excelled for distinct targets like birds. Automated detection of floating marine debris using self-supervised methods represents a scalable approach for monitoring the distribution and transport of macro-plastic litter that ultimately fragments into microplastics in Arctic waters.
Monitoring marine litter in the Arctic is crucial for environmental assessment, yet automated methods are needed to process large volumes of visual data. This study develops and compares two distinct machine learning approaches to automatically detect floating marine litter, birds, and other anomalies from ship-based optical imagery captured in the Barents and Kara seas. We evaluated a supervised Visual Object Detection (VOD) model (YOLOv11) against a self-supervised classification approach that combines a Momentum Contrast (MoCo) framework with a ResNet50 backbone and a CatBoost classifier. Both methods were trained and tested on a dataset of approximately 10,000 manually annotated sea surface images. Our findings reveal a significant performance trade-off between the two techniques. The YOLOv11 model excelled in detecting clearly visible objects like birds with an F1-score of 73%, compared to 67% for the classification method. However, for the primary and more challenging task of identifying marine litter, which demonstrates less clear visual representation in optical imagery, the self-supervised approach was substantially more effective, achieving a 40% F1-score, versus the 10% obtained for the VOD model. This study demonstrates that, while standard object detectors are effective for distinct objects, self-supervised learning strategies can offer a more robust solution for detecting less-defined targets like marine litter in complex sea-surface imagery.