Computer and Modernization ›› 2020, Vol. 0 ›› Issue (07): 111-116.doi: 10.3969/j.issn.1006-2475.2020.07.021

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Particle Size Detection of Sandstone Images Based on Full Convolutional Network


  1. (School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)
  • Online:2020-07-06 Published:2020-07-15

Abstract: In order to segment the tightly adhering sandstone and obtain the particle size of sandstone accurately, a particle size measurement method based on two-stage deep learning is proposed. This method uses image processing technology to preprocess the sandstone image, and then uses the first-stage segmentation model to segment the sandstone objects. After morphological processing of segmented objects, as many sandstone objects are connected closely, the second-stage separation model is adopted to separate the sandstone objects, then the result graph of segmented and separated is obtained. Finally, the longest diameters of the sandstone objects are calculated and the average particle size of the sandstone image is obtained. Experiments show that this algorithm can segment the closely connected sandstone objects quickly and accurately, and improve the speed and accuracy of sandstone particle size calculation.

Key words: sandstone image, particle size detection, semantic segmentation, computer vision, fully convolution network

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