The rapidly growing concern of marine microplastic pollution has drawn attention globally.
Microplastic particles are normally subjected to visual characterization before more sophisticated chemical analyses. However, the misidentification rate of current visual inspection approaches remains high.
This study proposed a state-of-the-art deep learning-based approach to locate, classify, and segment large marine microplastic particles (fiber, fragment, pellet, and rod). A microplastic dataset including 3000 images was established to train and validate this algorithm.
