We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
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.
Sign in to start a discussion.
More Papers Like This
GANsemble for Small and Imbalanced Data Sets: A Baseline for Synthetic Microplastics Data
Researchers developed a generative adversarial network (GAN) ensemble approach to generate synthetic training data for microplastic image classification, addressing the challenge that real microplastic image datasets are small and imbalanced by polymer type. The synthetic augmentation improved classifier accuracy and recall, particularly for underrepresented plastic categories.
Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
Researchers applied dual deep learning models (YOLOv8, YOLOv11, and several CNN architectures) to detect and classify microplastics in water, finding that these AI approaches could accurately identify plastic types across both aquatic and non-aquatic datasets.
Microplastic Identification Using AI-Driven Image Segmentation and GAN-Generated Ecological Context
Researchers built an AI-powered image segmentation system that can automatically identify microplastics in microscopic photos, then used a generative AI model to create synthetic training images to improve its accuracy. The system reached an F1 score of 0.91, outperforming a model trained without generated data, pointing toward faster and cheaper microplastic identification compared to current expert-driven methods.
Balancing Feature Symmetry: IFEM-YOLOv13 for Robust Underwater Object Detection Under Degradation
This paper presents IFEM-YOLOv13, a deep learning detection system designed to overcome image degradation challenges in underwater object detection. Innovations including adaptive optical compensation and feature enhancement modules improved detection accuracy for small and partially obscured targets including microplastic debris.
An Image Analysis of Coastal Debris Detection -Detection of microplastics using deep learning-
Researchers developed a deep learning-based coastal debris detection system using YOLOv7 and the SAHI vision library to identify microplastics in image data collected from shorelines. The system demonstrated effective detection performance and offers a scalable approach for automated monitoring of microplastic litter in coastal environments.