Bulk Rate Statistical Method Based on an Optimised Algorithm for Blasted Ore Image Segmentation

doi:10.12068/j.issn.1005-3026.2023.05.013

Abstract

Abstract: The ore image segmentation effect is limited by factors such as lighting conditions， targets density and low contrast of edges， resulting in low statistical accuracy of the bulk rate. Anqian Mine blasted ore image was used as the data source， firstly the bilateral filtering algorithm was used to remove the feature-enhanced image noise， then the adaptive thresholding algorithm and HED (holistically-nested edge detection) algorithm were used to initially segment the ore image， and then the morphology and connectivity removal algorithm were used to remove the segmentation hole formed by the texture of the ore surface. The results were further fused and a distance-based watershed algorithm was introduced to eliminate the under-segmentation of the ore image， and finally the optimal segmentation of the ore image was achieved. The results show that the proposed method can effectively improve the accuracy of blasted ore image segmentation， achieve accurate statistics of blasting bulk rate in open pit mines， and provide technical support for intelligent evaluation of blasting effects.

Key words: blasting bulk rate; ore image segmentation; HED (holistically-nested edge detection) algorithm; adaptive thresholding algorithm; watershed algorithm

CLC Number:

P232

MAO Ya-chun， FAN Shuo， CAO Wang， LI Shi. Bulk Rate Statistical Method Based on an Optimised Algorithm for Blasted Ore Image Segmentation[J]. Journal of Northeastern University(Natural Science), 2023, 44(5): 705-711.

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