Zero-Shot Fibers Detection in Scanning Electron Micrographs

The release of natural and artificial microfibers during laundering is considered one of the major sources of pollution in oceans. Quantifying microfibers is therefore necessary, and microscopy is the most used technique for any detection purpose. Scanning Electron Microscopy (SEM) can detect micro/nano scale objects, but related image analysis has proven more challenging than in traditional optical microscopy. Traditionally, trained experts count microfibers during washing cycles, but this task is tedious, subjective, and challenging. The development and application of automated techniques for extracting relevant image features has become necessary, at least to support the final human counting. Unfortunately, this area of research has been poorly investigated so far since the microfibers aspect has a large variability. This makes it difficult to transfer knowledge from human to machine. In this paper, a large AI model is applied, to the best of our knowledge, for the first time for fibers detection in SEM images. Its validity has been tested on a set of micrographs of wastewater from 4 washing cycles of cotton fabrics and 1 washing cycle of polyamide fabrics. Besides, the code and the annotated dataset of micrographs are also provided for research purposes.