Mineral image segmentation plays a vital role in the realization of machine vision based intelligent ore sorting equipment. However, the existing image segmentation methods still cannot effectively solve the problem of adhesion and overlap between mineral particles, and the segmentation performance of small and irregular particles still needs to be improved. To overcome these bottlenecks, we propose a deep learning based image segmentation method to segment the key areas in mineral images using morphological transformation to process mineral image masks. This investigation explores four aspects of the deep learning-based mineral image segmentation model, including backbone selection, module configuration, loss function construction, and its application in mineral image classification. Specifically, referring to the designs of U-Net, FCN, Seg Net, PSP Net, and DeepLab Net, this experiment uses different backbones as Encoder to building ten mineral image segmentation models with different layers, structures, and sampling methods. Simultaneously, we propose a new loss function suitable for mineral image segmentation and compare CNNs-based segmentation models' training performance under different loss functions. The experiment results show that the proposed mineral image segmentation has excellent segmentation performance, effectively solves adhesion and overlap between adjacent particles without affecting the classification accuracy. By using the Mobile Net as backbone, the PSP Net and DeepLab can achieve a high segmentation performance in mineral image segmentation tasks, and the 15 × 15 is the most suitable size for erosion element structure to process the mask images of the segmentation models.

矿物图像分割在基于机器视觉的智能矿石分选设备的实现中起着至关重要的作用。然而，现有的图像分割方法仍不能有效解决矿物颗粒之间的粘连和重叠问题，对细小不规则颗粒的分割性能仍有待提高。为了克服这些瓶颈，我们提出了一种基于深度学习的图像分割方法，使用形态变换来处理矿物图像掩码来分割矿物图像中的关键区域。本研究探讨了基于深度学习的矿物图像分割模型的四个方面，包括主干选择、模块配置、损失函数构建及其在矿物图像分类中的应用。具体参考U-Net、FCN、Seg Net的设计，PSP Net，DeepLab Net，本次实验使用不同的backbone作为Encoder，构建了10个不同层次、不同结构、不同采样方式的矿物图像分割模型。同时，我们提出了一种适用于矿物图像分割的新损失函数，并比较了基于 CNN 的分割模型在不同损失函数下的训练性能。实验结果表明，所提出的矿物图像分割具有优异的分割性能，有效解决了相邻颗粒之间的粘附和重叠问题，且不影响分类精度。通过使用 Mobile Net 作为主干，PSP Net 和 DeepLab 在矿物图像分割任务中可以获得很高的分割性能，