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The quantification of microplastics is a needed task to monitor its evolution and model its behavior. However, it is a time demanding task traditionally performed using expensive equipment. In this paper, an architecture based on deep learning networks is presented with the aim of automatically count and classify microplastic particles in the range of 1-5 mm from pictures taken with a digital camera or a mobile phone with a resolution of 16 million pixels or higher. The proposed architecture comprises a first stage, implemented with the
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