@Article{cmc.2020.09806,
AUTHOR = {Hui Li, Chengwei Pan, Ziyi Chen, Aziguli Wulamu, Alan Yang},
TITLE = {Ore Image Segmentation Method Based on U-Net and Watershed},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {563--578},
URL = {http://www.techscience.com/cmc/v65n1/39583},
ISSN = {1546-2226},
ABSTRACT = {Ore image segmentation is a key step in an ore grain size analysis based on 
image processing. The traditional segmentation methods do not deal with ore textures and 
shadows in ore images well Those methods often suffer from under-segmentation and 
over-segmentation. In this article, in order to solve the problem, an ore image 
segmentation method based on U-Net is proposed. We adjust the structure of U-Net to 
speed up the processing, and we modify the loss function to enhance the generalization of 
the model. After the collection of the ore image, we design the annotation standard and 
train the network with the annotated image. Finally, the marked watershed algorithm is 
used to segment the adhesion area. The experimental results show that the proposed 
method has the characteristics of fast speed, strong robustness and high precision. It has 
great practical value to the actual ore grain statistical task.},
DOI = {10.32604/cmc.2020.09806}
}