Superpixel Image Segmentation-Based Particle Size Distribution Analysis of Fragmented Rock

Autor: Haojie Ding, Zhen Yang, Li Guo, Minjie Lian
Jazyk: angličtina
Rok vydání: 2021
Předmět:
010504 meteorology & atmospheric sciences
General Computer Science
Computer science
image denoising
Noise reduction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
superpixel generated image
02 engineering and technology
01 natural sciences
Texture (geology)
Image (mathematics)
0202 electrical engineering
electronic engineering
information engineering
General Materials Science
image enhancement
Electrical and Electronic Engineering
0105 earth and related environmental sciences
business.industry
General Engineering
Wavelet transform
Pattern recognition
Image segmentation
TK1-9971
Photogrammetry
020201 artificial intelligence & image processing
Artificial intelligence
Particle size
Granularity
Electrical engineering. Electronics. Nuclear engineering
business
Digital image processing
granularity distribution
Zdroj: IEEE Access, Vol 9, Pp 59048-59058 (2021)
ISSN: 2169-3536
Popis: Research on the particle size of blast piles has always been an essential issue in mining engineering. Reasonable blasting parameters can reduce mining costs and reduce the workload of secondary crushing, which can significantly improve mining efficiency. The usual particle size analysis methods include the sieving method, the large particle size statistical method and other manual measurement methods. Nevertheless, these methods have the disadvantages of a high labor intensity, low efficiency and low precision. This paper analyzes UAV image information based on the single-picture photogrammetry method of computer image processing technology. A two-dimensional empirical wavelet transform (EWT) is used for image noise reduction. The nonlocal multiscale fractional differential (NMFD) enhances the texture of dark images and uses superpixel image segmentation technology so that the processed image can meet the granularity statistical study requirements of blast piles. The research results show that the accuracy of the ore particle size distribution by the method proposed in this paper is more than 90%.
Databáze: OpenAIRE
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