Design and Implementation of a Microplastic Detection and Classification System Supported by Deep Learning Algorithm

Abstract

Microparticles are challenging to detect due to their small size and can harm living things when exposed. Especially microplastics are one of the harmful microparticles. For this reason, detecting microplastics in a vital consumer item such as water is essential. Machine learning in the detection method allows the learning of different types and sizes of microplastics, allowing such systems to work unremittingly in real time. The present study has designed a low-budget, high-accuracy device with a deep learning algorithm that can autonomously classify microplastics according to their size and type. Three lasers with dual beam wavelengths of 405nm, 655nm, and 534nm-807nm, frequently used in laser pointers, are light sources in the sensor. The beams formed by the lasers were combined employing a beam combiner, allowing beams to emerge from a single point. Classification success of up to 100% has been achieved, thanks to the different interference patterns of light sources of various wavelengths. 10µm polystyrene, 8µm polystyrene, and 8µm melamine prepared in different constancy were used as samples in the experiments.