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Size Measurement of Stone Images Based on Improved UNet

EasyChair Preprint no. 9816

8 pagesDate: March 2, 2023


To address the current problem of calculating stone grain size in the field of sand and gravel aggregates, image segmentation of stone targets is achieved by stone images, and the grain length of stone targets is finally obtained. By pre-processing the target stone images, the pre-processed stone images are segmented and predicted using deep learning image processing techniques, and the predicted result maps are subjected to morphological and image binarization operations for subsequent stone particle size calculation. The algorithm is implemented to delineate the assignment of individual stone regions and to find the boundary coordinate points of individual stone image regions, and to calculate the image grain size length of stones from them. The true grain length of the stone is calculated by the proportional mapping relationship between the camera and the pixel length of the stone taken and the real stone length.Through experiments, this operation procedure can segment and calculate the grain length of stones quickly and accurately.

Keyphrases: grain size measurement, Mapping, semantic segmentation, stone

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ning Chen and Xinkai Ma and Jun Peng and Shangzhu Jin and Xiao Wu and Yan Wu and Haixia Luo},
  title = {Size Measurement of Stone Images Based on Improved UNet},
  howpublished = {EasyChair Preprint no. 9816},

  year = {EasyChair, 2023}}
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