Image segmentation methods have been utilized to determine the particle size distribution of crushed ores. Due to the complex working environment, high-powered computing equipment is difficult to deploy. At the same time, the ore distribution is stacked, and it is difficult to identify the complete features. To address this issue, an effective box-supervised technique with texture features is provided for ore image segmentation that can identify complete and independent ores. Firstly, a ghost feature pyramid network (Ghost-FPN) is proposed to process the features obtained from the backbone to reduce redundant semantic information and computation generated by complex networks. Then, an optimized detection head is proposed to obtain the feature to maintain accuracy. Finally, Lab color space (Lab) and local binary patterns (LBP) texture features are combined to form a fusion feature similarity-based loss function to improve accuracy while incurring no loss. Experiments on MS COCO have shown that the proposed fusion features are also worth studying on other types of datasets. Extensive experimental results demonstrate the effectiveness of the proposed method, which achieves over 50 frames per second with a small model size of 21.6 MB. Meanwhile, the method maintains a high level of accuracy compared with the state-of-the-art approaches on ore image dataset. The source code is available at \url{https://github.com/MVME-HBUT/OREINST}.

翻译：图像分割方法已被用于确定破碎矿石的粒度分布。由于复杂的工作环境，高性能计算设备难以部署。同时，矿石分布呈现堆叠状态，难以识别完整特征。为解决该问题，提出了一种有效的基于纹理特征的框监督矿石图像分割技术，能够识别完整且独立的矿石。首先，提出了一种幽灵特征金字塔网络（Ghost-FPN）以处理主干网络提取的特征，从而减少复杂网络产生的冗余语义信息与计算量。其次，设计了优化检测头来获取特征以保持精度。最后，结合Lab颜色空间（Lab）与局部二值模式（LBP）纹理特征，构建基于融合特征相似性的损失函数，在无性能损失的情况下提升精度。在MS COCO上的实验表明，所提融合特征在其他类型数据集上同样具有研究价值。大量实验结果验证了该方法的有效性，其以21.6 MB的小模型尺寸实现了超过50帧每秒的处理速度。同时，在矿石图像数据集上，该方法与现有最优方法相比保持了高精度水平。源代码见 \url{https://github.com/MVME-HBUT/OREINST}。