Research Article
Graphite Ore Grade Classification Algorithm Based on Multi-scale Fused Image Features
@INPROCEEDINGS{10.1007/978-3-031-55471-1_14, author={Jionghui Wang and Yaokun Liu and Xueyu Huang and Shaopeng Chang}, title={Graphite Ore Grade Classification Algorithm Based on Multi-scale Fused Image Features}, proceedings={Mobile Networks and Management. 13th EAI International Conference, MONAMI 2023, Yingtan, China, October 27-29, 2023, Proceedings}, proceedings_a={MONAMI}, year={2024}, month={3}, keywords={feature aggregation texture features depthwise convolution residual network attention mechanism global response normalization (GRN) graphite ore}, doi={10.1007/978-3-031-55471-1_14} }- Jionghui Wang
Yaokun Liu
Xueyu Huang
Shaopeng Chang
Year: 2024
Graphite Ore Grade Classification Algorithm Based on Multi-scale Fused Image Features
MONAMI
Springer
DOI: 10.1007/978-3-031-55471-1_14
Abstract
Aiming at the problems of complex pre-processing and expensive equipment in chemical detection of graphite ore grade, a graphite ore identification and classification method based on fusing multi-scale image features is proposed. In the feature extraction stage, a deep convolutional neural network and a residual network model based on spatial attention mechanism are constructed to improve the learning ability of local and global features of graphite ore images; in the feature aggregation stage, a global response normalization technique is introduced to achieve more accurate graphite ore grade recognition, and the accuracy of the model reaches 93.401% and the macro F1 reaches 93.086%, which is better than the single The accuracy of the model reaches 93.401% and the macro F1 reaches 93.086%, which is better than the traditional machine learning methods with single feature. The experimental results show that the features extracted by different methods can describe the texture and edge information of graphite ore, and the proposed method has better extraction ability in terms of local features and global features of graphite ore images, and achieves more accurate graphite ore grade recognition with good robustness.
