Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning
This addresses the need for efficient methods to study plastic pollution, which impacts health and environmental systems, by providing a foundational dataset for researchers, though it is incremental as it applies existing algorithms to new data.
The paper tackled the problem of detecting and classifying micro- and nanoplastics from consumer products by introducing MiNa, an open-source dataset of scanning electron microscopy images, and demonstrated the application of state-of-the-art object detection algorithms on it.
Plastic pollution presents an escalating global issue, impacting health and environmental systems, with micro- and nanoplastics found across mediums from potable water to air. Traditional methods for studying these contaminants are labor-intensive and time-consuming, necessitating a shift towards more efficient technologies. In response, this paper introduces micro- and nanoplastics (MiNa), a novel and open-source dataset engineered for the automatic detection and classification of micro and nanoplastics using object detection algorithms. The dataset, comprising scanning electron microscopy images simulated under realistic aquatic conditions, categorizes plastics by polymer type across a broad size spectrum. We demonstrate the application of state-of-the-art detection algorithms on MiNa, assessing their effectiveness and identifying the unique challenges and potential of each method. The dataset not only fills a critical gap in available resources for microplastic research but also provides a robust foundation for future advancements in the field.