Open Access

ARTICLE

Ore Image Segmentation Method Based on U-Net and Watershed

Hui Li¹, Chengwei Pan^{2, 3}, Ziyi Chen¹, Aziguli Wulamu^{2, 3, *}, Alan Yang⁴

1 School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
2 School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
3 Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China.
4 Amphenol AssembleTech, Houston, 77070, USA.

* Corresponding Author: Aziguli Wulamu. Email: .

Computers, Materials & Continua 2020, 65(1), 563-578. https://doi.org/10.32604/cmc.2020.09806

Received 19 January 2020; Accepted 04 May 2020; Issue published 23 July 2020

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.

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Citations