Ore Image Segmentation Method of Conveyor Belt Based on U-Net and Res_UNet Models

doi:10.12068/j.issn.1005-3026.2019.11.019

Abstract

Abstract: Aiming at the problem of inaccurate segmentation caused by the adhesion and edge blurring of the ore image in the conveyor belt， a method for ore image segmentation of conveyor belt based on U-Net and Res_UNet models is proposed. Firstly， the image to be segmented is processed by gray-scale， median filtering and adaptive histogram equalization， and then the pre-trained U-Net model is used to extract the image contour. Then， after binary image contour， the pre-trained Res_UNet model is used for contour optimization. Finally， OpenCV is used to obtain the segmentation result. Compared with watershed algorithm based on morphological reconstruction and NUR method for 10 test images， the proposed method for ore contour detection and optimization based on deep learning is more accurate， proving its effectiveness for image segmentation of conveyor belt ores.

Key words: conveyor belt, U-Net, Res_UNet, ore segmentation, deep learning

CLC Number:

TD672

LIU Xiao-bo， ZHANG Yu-wei. Ore Image Segmentation Method of Conveyor Belt Based on U-Net and Res_UNet Models[J]. Journal of Northeastern University Natural Science, 2019, 40(11): 1623-1629.

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