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
https://github.com/MVME-HBUT/OREINST.

提供了一种具有纹理特征的有效的箱式监督技术用于矿石图像分割，该技术能够识别完整和独立的矿石，并通过使用 Ghost-FPN，优化的检测头以及融合特征相似性损失函数来提高准确性。这种方法在矿石图像数据集上保持了高水平的准确性。