论文标题
基于Densenet和Gaussian工艺自动识别煤炭和岩石/螺栓
Automatic Identification of Coal and Rock/Gangue Based on DenseNet and Gaussian Process
论文作者
论文摘要
为了提高煤炭的纯度并防止对煤矿机器的损坏，有必要在地下煤矿中识别煤炭和岩石。同时，需要净化开采的煤炭以去除岩石和螺栓。这两个程序是由大多数煤矿的工人手动操作的。自动识别和净化的实现不仅有助于煤矿的自动化，而且还可以确保工人的安全。我们讨论了使用基于图像的方法区分它们的可能性。为了找到可以在两种情况下都使用的解决方案，提出了将Densenet提取到高斯工艺的模型，该模型是在表面上拍摄的图像并在地下拍摄的图像上获得高精度的。这表明我们的方法在少量学习中具有强大的功能，例如对煤炭和岩石/曼内斯的识别，可能有益于实现煤矿中的自动化。
To improve the purity of coal and prevent damage to the coal mining machine, it is necessary to identify coal and rock in underground coal mines. At the same time, the mined coal needs to be purified to remove rock and gangue. These two procedures are manually operated by workers in most coal mines. The realization of automatic identification and purification is not only conducive to the automation of coal mines, but also ensures the safety of workers. We discuss the possibility of using image-based methods to distinguish them. In order to find a solution that can be used in both scenarios, a model that forwards image feature extracted by DenseNet to Gaussian process is proposed, which is trained on images taken on surface and achieves high accuracy on images taken underground. This indicates our method is powerful in few-shot learning such as identification of coal and rock/gangue and might be beneficial for realizing automation in coal mines.
