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The nanoparticle size and distribution information in the SEM images ofsilicon crystals are generally counted by manual methods. The realization ofautomatic machine recognition is significant in materials science. This paperproposed a superposition partitioning image recognition method to realizeautomatic recognition and information statistics of silicon crystalnanoparticle SEM images. Especially for the complex and highly aggregatedcharacteristics of silicon crystal particle size, an accurate recognition stepand contour statistics method based on morphological processing are given. Thismethod has technical reference value for the recognition of Monocrystallinesilicon surface nanoparticle images under different SEM shooting conditions.Besides, it outperforms other methods in terms of recognition accuracy andalgorithm efficiency.

A Superimposed Divide-and-Conquer Image Recognition Method for SEM  Images of Nanoparticles on The Surface of Monocrystalline silicon with High  Aggregation Degree