摘要
随着当今世界的飞速发展,各种高楼大厦林立,对于铁以及钢材的需求日益增加,随之而来的对于铁矿石的需求也逐年上涨,由于铁矿产业是对不可再生资源的开采,因此对铁矿石进行分类,提高铁矿石的利用效率就变得极其重要。为了提高铁矿石的分类速度以及分类准确率,文中提出了一种基于卷积神经网络和注意力机制的铁矿石图像分类方法。该方法不需要对输入的图像进行手工提取特征,通过深度学习模型框架来弥补传统图像处理算法的不足,实现对铁矿石准确、高效的分类,可以较好地识别多种类型的铁矿石。对于铁矿石的3种基本类型具有较好的分类效果以及准确率。实验结果证明,所提方法在数据集上的准确率达到88.46%,相比其他算法模型,其模型训练时间更短,性能更优。利用深度学习的方法,部署自动化铁矿石分类模型,对于社会发展有重要意义。
With the rapid development of the world today,a variety of high-rise buildings,the demand for iron and steel is increasing,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 framework,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 algorithm 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.
作者
吕一鸣
王激扬
LYU Yiming;WANG Jiyang(College of Artificial Intelligence,Shenyang University of Technology,Shenyang 110870,China)
出处
《计算机科学》
CSCD
北大核心
2024年第S01期549-554,共6页
Computer Science
关键词
卷积神经网络
注意力机制
深度学习
铁矿石分类
图像分类
Convolutional neural network
Attention mechanism
Deep learning
Iron ore classification
Image classification
