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SAM-Augmented Blending for Enhanced Microplastic Detection Using YOLO11
Summary
Researchers developed a synthetic data augmentation method using SAM-generated instance masks combined with the YOLOv11 object detection architecture to improve underwater microplastic detection in images. The approach significantly improved detection performance for small, sparsely labeled microplastic objects where real training data is scarce.
Underwater microplastic pollution poses a significant threat to marine ecosystems and human health, yet accurate detection remains challenging due to the scarcity of annotated data and the small size of target objects. This study presents a synthetic data augmentation approach to enhance underwater microplastic detection using the YOLOv11 object detection architecture. Instance masks are generated with the Segment Anything Model (SAM), enabling the creation of synthetic training data through blending and copy-paste techniques on diverse aquatic backgrounds. We train and evaluate five YOLOv11 models on a consistent real-world test set, demonstrating that all synthetic variants outperform the baseline model, with the SAM-blended (1BG) model achieving the highest mAP@0.5:0.95. These results highlight the effectiveness of SAM-guided synthetic augmentation in addressing data limitations and improving performance for underwater object detection tasks.