Microplastics Data

Microplastics data research focuses on developing efficient methods for detecting and quantifying microplastics across various environments, from aquatic surfaces to underwater settings and even urban areas. Current efforts heavily utilize deep learning, employing architectures like YOLO variants and other object detection algorithms, along with generative adversarial networks (GANs) to address data scarcity issues, particularly for smaller or imbalanced datasets. These advancements are crucial for improving the accuracy and scalability of microplastic monitoring, informing environmental management strategies and ultimately contributing to a better understanding of the pervasive impact of plastic pollution.