Statistics of Blasting Fragmentation Distribution Based on Computer Vision
Blasting fragmentation is an important index to evaluate the blasting effect of mines.The traditional fragmentation statistical method has the problems of low efficiency and many restrictions.The statistical method of blasting fragmentation based on computer vision has the advantages of high efficiency,accuracy and flexibility.Aiming at the problem of computer vision recognition of blasting piles fragmentation statistics,a morphologically optimized method of fragmentation statistical combining Mask B-R-CNN and HSV transform was proposed,and the blasting piles fragmentation statistics of the laboratory and the mine site of Anqian Mining Company were verified.The results show that compared with the screening method,the error of fragmentation distribution statistical method based on computer vision is less than 3%,which verifies the feasibility of this method for the blasting piles fragmentation statistics.The field experiment results of mine show that compared with the traditional method of extracting ore and rock area,this method is more accurate in extracting ore and fock area,and can be applied to the mine site of Anqian Mining Company.The cumulative probability curves of the three blasting fragtnentation distributions are similar,and the large fragmentation rates are 4.21%,3.37%and 3.12%respectively,and the blasting effectisbetter.