Iron Ore Image Classification Method Based on Improved Efficientnetv2
With the rapid development of the world today,a variety of high-rise buildings,the demand for iron and steel is in-creasing,and the demand for iron ore is also rising year by year.Because the iron ore industry is the exploitation of non-renewable resources,it is extremely important to classify iron ore and improve its utilization efficiency.In order to improve the classification speed and accuracy of iron ore,an iron ore image classification method based on convolutional neural network and attention me-chanism is proposed.It does not need to manually extract features from the input images.Through the deep learning model frame-work,it makes up for the shortcomings of traditional image processing algorithms,realizes accurate and efficient classification of iron ore,and can better identify various types of iron ore.It has good classification effect and accuracy for the three basic types of iron ore.Experiments show that the accuracy of the proposed method on the data set reaches 87.46％.Compared with other algo-rithm models,the model training time is shorter and the performance is better.Using deep learning methods to deploy automated iron ore classification models is of great significance to social development.
NETL
NSTL
万方数据
