Wang Wei, Li Qing, Xiao Chengyong, Zhang Dezheng, Miao Lei, Wang Li
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Key Laboratory of Knowledge Automation for Industrial Processes, University of Science and Technology Beijing, Ministry of Education, Beijing 100083, China.
Sensors (Basel). 2021 Apr 8;21(8):2615. doi: 10.3390/s21082615.
Particle size is the most important index to reflect the crushing quality of ores, and the accuracy of particle size statistics directly affects the subsequent operation of mines. Accurate ore image segmentation is an important prerequisite to ensure the reliability of particle size statistics. However, given the diversity of the size and shape of ores, the influence of dust and light, the complex texture and shadows on the ore surface, and especially the adhesion between ores, it is difficult to segment ore images accurately, and under-segmentation can be a serious problem. The construction of a large, labeled dataset for complex and unclear conveyor belt ore images is also difficult. In response to these challenges, we propose a novel, multi-task learning network based on U-Net for ore image segmentation. To solve the problem of limited available training datasets and to improve the feature extraction ability of the model, an improved encoder based on Resnet18 is proposed. Different from the original U-Net, our model decoder includes a boundary subnetwork for boundary detection and a mask subnetwork for mask segmentation, and information of the two subnetworks is fused in a boundary mask fusion block (BMFB). The experimental results showed that the pixel accuracy, Intersection over Union (IOU) for the ore mask (IOU_M), IOU for the ore boundary (IOU_B), and error of the average statistical ore particle size (ASE) rate of our proposed model on the testing dataset were 92.07%, 86.95%, 52.32%, and 20.38%, respectively. Compared to the benchmark U-Net, the improvements were 0.65%, 1.01%, 5.78%, and 12.11% (down), respectively.
粒度是反映矿石破碎质量的最重要指标，粒度统计的准确性直接影响矿山的后续作业。准确的矿石图像分割是确保粒度统计可靠性的重要前提。然而，鉴于矿石尺寸和形状的多样性、灰尘和光线的影响、矿石表面复杂的纹理和阴影，尤其是矿石之间的粘连，准确分割矿石图像很困难，分割不足可能是一个严重问题。构建用于复杂且不清晰的传送带矿石图像的大型标注数据集也很困难。针对这些挑战，我们提出了一种基于U-Net的新颖多任务学习网络用于矿石图像分割。为了解决可用训练数据集有限的问题并提高模型的特征提取能力，提出了一种基于Resnet18的改进编码器。与原始U-Net不同，我们的模型解码器包括一个用于边界检测的边界子网和一个用于掩码分割的掩码子网，并且两个子网的信息在边界掩码融合块（BMFB）中进行融合。实验结果表明，我们提出的模型在测试数据集上的像素准确率、矿石掩码的交并比（IOU_M）、矿石边界的交并比（IOU_B）以及平均统计矿石粒度误差（ASE）率分别为92.07%、86.95%、52.32%和20.38%。与基准U-Net相比，改进分别为0.65%、1.01%、5.78%和12.11%（下降）。
