Research on Segmentation and Classification Methods of Mixed Overlapped Particle Images

CHEN Zong-yuan; ZHANG Lei-lei; ZHAO Ning-ning; SU Ming-xu

doi:10.3969/j.issn.1000-1158.2022.06.07

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Acta Metrologica Sinica ›› 2022, Vol. 43 ›› Issue (6) : 745-752. DOI: 10.3969/j.issn.1000-1158.2022.06.07

Research on Segmentation and Classification Methods of Mixed Overlapped Particle Images

CHEN Zong-yuan,ZHANG Lei-lei,ZHAO Ning-ning,SU Ming-xu

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Abstract

Mask R-CNN was introduced to overcome the segmentation difficulties of traditional image processing algorithms for overlapped particle images.By adjusting residual network ResNet-101 to accelerate training, a double FPN structure was proposed to achieve global feature fusion, and soft-NMS was used to avoid o overlapped particle missing detection.A particle overlapped image experiment system was designed to acquire single spherical, spherical and irregular mixed multi-dispersed overlapped particle images for segmentation analysis.The experimental results show that the present classification accuracy is 91%, and the recall rate is 92%, which are both better than the traditional algorithms.When applied to the real-time measurement of crystallization and bubbles in the crystallization process of citric acid monohydrate, the method yields the errors around 3.8% for median diameter and -1.6% for the counting number of crystal particles.The proposed method provides a clue for analysis of overlapped mixed particle images, which is expected to solve the problems of image analysis at late stage of the crystallization process and eliminate the interference of bubbles involved during real-time monitoring.

Key words

metrology / mixed particle / overlapped image / particle segmentation / size distribution / citric acid monohydrate / crystallization / deep learning

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CHEN Zong-yuan,ZHANG Lei-lei,ZHAO Ning-ning,SU Ming-xu. Research on Segmentation and Classification Methods of Mixed Overlapped Particle Images[J]. Acta Metrologica Sinica. 2022, 43(6): 745-752 https://doi.org/10.3969/j.issn.1000-1158.2022.06.07

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