A Mask R-CNN based particle identification for quantitative shape evaluation of granular materials

Dangfu Yang, Xiang Wang, Haoran Zhang, Zhen yu Yin, Dong Su, Jun Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Particle identification and shape evaluation of granular materials from their realistic packing images are challenging and of great interest to many engineers and researchers. In this study, a systematic tool is developed based on computing techniques, including deep learning and computational geometry. First, image datasets of the target granular particles with well-labeled masks are established. The Mask Region Convolutional Neural Network (Mask R-CNN) is employed to implement the end-to-end instance segmentation and contour extraction of particles on different realistic images. Since Mask R-CNN models have several different feature extraction backbones, the optimal model is selected and then trained on the established datasets using transfer learning technique. After the particles are successfully identified from images of cobble and ballast, the elongation, angularity, and roughness are evaluated and the statistical shape analysis is conducted. The proposed method has strong generalization ability, especially for densely-packed particles.

Original languageEnglish
Pages (from-to)296-305
Number of pages10
JournalPowder Technology
Volume392
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Granular particle
  • Instance segmentation
  • Mask R-CNN
  • Particle detection
  • Shape evaluation

ASJC Scopus subject areas

  • Chemical Engineering(all)

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