Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning

For example, a U-Net [31] model can be used for some studies have utilized manually annotated images for deep semantic segmentation, and a Convolutional Neural Network learning applications involving microplastics, their datasets are (CNN) can then classify the segmented pixels, as demonstrated not publicly accessible [22], [23], [25]. Notably, there is only in [22], [24]. It is also possible to perform instance segmentation one other open-source Scanning Electron Microscopy (SEM) directly from the start. For instance, a Mask R-CNN dataset on microplastics, presented in [24], which categorizes model can simultaneously identify regions of interest, classify particles by shape (e.g., fragments, fibers, and beads) and each detected object, and generate a mask for each instance, features a more limited size distribution. These contributions as shown by [23]. Additionally, Faster R-CNN, primarily used not only address the urgent environmental issue of microplastic for object detection, has been applied to microscopic images to contamination but also set a new benchmark for detecting and classify microplastics into two polymer types [25]. Given the analyzing microplastics in aquatic environments, paving the nature of our dataset, where overlapping and crowded MNPs way for future innovations in the field.