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

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. (c) 2021 Published by Elsevier B.V.

Automated summary

This study developed a systematic tool based on deep learning and computational geometry to evaluate and identify the shape of granular materials. By establishing image datasets with labeled masks and employing the Mask R-CNN model, successful identification and shape analysis of particles were achieved.