Ore image segmentation method using U-Net and Res_Unet convolutional networks

Image segmentation has been increasingly used to identify the particle size distribution of crushed ore; however, the adhesion of ore particles and dark areas in the images of blast heaps and conveyor belts usually results in lower segmentation accuracy. To overcome this issue, an image segmentation method UR based on deep learning U-Net and Res_Unet networks is proposed in this study. Gray-scale, median filter and adaptive histogram equalization techniques are used to preprocess the original ore images captured from an open pit mine to reduce noise and extract the target region. U-Net and Res_Unet are utilized to generate ore contour detection and optimization models, and the ore image segmentation result is illustrated by OpenCV. The efficiency and accuracy of the newly proposed UR method is demonstrated and validated by comparing with the existing image segmentation methods.