The invention relates to a
conveyor belt ore rock particle
image segmentation method. The method comprises the following steps: 101, preprocessing a to-be-segmented
conveyor belt ore rock particle image, and inputting the to-be-segmented
conveyor belt ore rock particle image into a pre-trained first
convolutional neural network model to obtain a first contour map; 102, inputting the first contourmap into a pre-trained second
convolutional neural network model to obtain a second contour map; 103, performing binarization
processing on the second contour map by using a preset threshold, and performing morphological closed operation
processing on the binarized map to obtain a third contour map; 104, obtaining the contour, the
minimum bounding rectangle of the contour and the area of the contour in the third contour map, screening by using the
minimum bounding rectangle of the contour and the area of the contour, and drawing the screened contour as a segmentation map. According to the method,
deep learning is utilized to realize conveyor belt ore rock
image contour detection and ore rock size distribution automatic statistics, the requirement for image definition is reduced,
image segmentation is accurate, and application is convenient.