Xiao Dong, Liu Xiwen, Le Ba Tuan, Ji Zhiwen, Sun Xiaoyu
Information Science and Engineering School, Northeastern University, Shenyang 110004, China.
Liaoning Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Northeastern University, Shenyang 110819, China.
Sensors (Basel). 2020 Sep 2;20(17):4979. doi: 10.3390/s20174979.
The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmentation method is a convenient and fast choice. However, due to the influence of dust, light and uneven color and texture, the traditional ore image segmentation methods are prone to oversegmentation and undersegmentation. In order to solve these problems, this paper proposes an ore image segmentation model called RDU-Net (R: residual connection; DU: DUNet), which combines the residual structure of convolutional neural network with DUNet model, greatly improving the accuracy of image segmentation. RDU-Net can adaptively adjust the receptive field according to the size and shape of different ore fragments, capture the ore edge of different shape and size, and realize the accurate segmentation of ore image. The experimental results show that compared with other U-Net and DUNet, the RDU-Net has significantly improved segmentation accuracy, and has better generalization ability, which can fully meet the requirements of ore fragment size detection in the concentrator.
选矿厂传送带上矿石碎块的尺寸，不仅是验证破碎过程的主要指标，还会影响矿山的生产效率、运营成本乃至生产安全。为获取传送带上矿石碎块的尺寸，图像分割方法是一种便捷快速的选择。然而，由于灰尘、光线以及颜色和纹理不均的影响，传统的矿石图像分割方法容易出现过分割和欠分割现象。为解决这些问题，本文提出一种名为RDU-Net（R：残差连接；DU：DUNet）的矿石图像分割模型，该模型将卷积神经网络的残差结构与DUNet模型相结合，极大地提高了图像分割的准确率。RDU-Net能够根据不同矿石碎块的尺寸和形状自适应调整感受野，捕捉不同形状和尺寸的矿石边缘，实现矿石图像的精准分割。实验结果表明，与其他U-Net和DUNet相比，RDU-Net的分割准确率有显著提高，且具有更好的泛化能力，能够充分满足选矿厂矿石碎块尺寸检测的要求。
