The invention relates to a blast heap ore rock particle
image segmentation method. The method comprises the following steps: S1, inputting a to-be-segmented ore rock particle image of the blasting
muck pile into a pre-trained first
convolutional neural network model to obtain a first probability graph and a first profile graph; S2, correcting the first probability graph by using the perimeter andthe area of the closed contour to obtain a first corrected probability graph; S3, inputting the first correction probability graph into a pre-trained second
convolutional neural network model to obtain a second probability graph and a second contour graph; S4, obtaining an (n + 1) th probability graph by means of a second
convolutional neural network until the resolution ratio of the total area ofall contours in the (n + 1) th contour graph to the first probability graph is greater than or equal to 1; S5, adding the nth contour map and the (n + 1) th contour map to serve as a segmentation map; according to the method, the contour is extracted by adopting the convolutional network of
deep learning, the continuity is good, the number of miscellaneous points is small, the edges of the adhered ore rock particles are accurately segmented, adjustment of complex parameters is avoided, the segmentation precision is high, and the practicability is good.