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Alternative TitleResearch on Iron Ore Grade Identification Technology Based on YOLOv4-tiny
何文轩1; 荆洪迪2; 柳小波2; 于健洋1; 孙效玉1
Source Publication金属矿山
Contribution Rank2
Funding Organization国家自然科学基金项目(编号:51674061)
Keyword铁矿 矿石品位 图像识别 YOLOv4-tiny 特征识别


Other Abstract

In order to realize the intelligent recognition of different iron ore grades and the loading of models on portable devices,data enhancement processing is carried out on different grades of different iron ore and YOLOv4-tiny is selected as the neural network algorithm for training. YOLOv4-tiny deep learning neural network framework,using CSPdarknet53_tiny as the backbone extraction network and combined with FPN for feature extraction and learning of rock images. In the training process,migration learning ideas and early stopping methods are used to accelerate training to generate iron ore grades Identify the model. Finally,it is verified by the test set that the model has an image recognition accuracy rate of greater than 91% for each ore grade. The recognition of images and videos taken in different environments also exceeds 80%. The model can distinguish iron ore of different grades well,and the test proves that the model is robust.

Document Type期刊论文
Corresponding Author何文轩
Recommended Citation
GB/T 7714
何文轩,荆洪迪,柳小波,等. 基于YOLOv4-tiny的铁矿石品位识别技术研究[J]. 金属矿山,2021(10):150-154.
APA 何文轩,荆洪迪,柳小波,于健洋,&孙效玉.(2021).基于YOLOv4-tiny的铁矿石品位识别技术研究.金属矿山(10),150-154.
MLA 何文轩,et al."基于YOLOv4-tiny的铁矿石品位识别技术研究".金属矿山 .10(2021):150-154.
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