Ore Image Segmentation Method Based on U-Net and Watershed
Hui Li, Chengwei Pan, Ziyi Chen, Aziguli Wulamu, Alan Yang
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.