A Graphite Ore Grade Recognition Method Based on Improved Inception-ResNet-v2 Model
With the rapid advancement of technology, intelligent identification of graphite ore grade in graphite mines has emerged as an essential requirement. To address the variability and low timeliness of traditional manual methods and the limited accuracy of deep learning due to image complexity and feat...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10921646/ |
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| author | Xueyu Huang Renjie Pan Jionghui Wang |
| author_facet | Xueyu Huang Renjie Pan Jionghui Wang |
| author_sort | Xueyu Huang |
| collection | DOAJ |
| description | With the rapid advancement of technology, intelligent identification of graphite ore grade in graphite mines has emerged as an essential requirement. To address the variability and low timeliness of traditional manual methods and the limited accuracy of deep learning due to image complexity and feature similarity, we propose an improved Inception-ResNet-v2 model for graphite ore grade recognition. Key improvements include: 1) To enhance the extraction of global feature information from graphite mine data, a global average pooling branch is incorporated into the Inception-resnet architecture. 2) Incorporating a <inline-formula> <tex-math notation="LaTeX">$1\times 1$ </tex-math></inline-formula> convolutional layer at the tail of the model to control channel dimensions and employing the LeakyReLU activation function to address the limitations of the ReLU activation function. 3) Designing an LDP-Conv structure to replace certain <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> convolutions and incorporating a channel attention mechanism to improve feature capture. 4) Optimizing the Stem module to expand the early-stage receptive field and reconstructing the network architecture. Experiments on the self-made graphite ore dataset showed improvements of 2.77% in Accuracy, 2.96% in Precision, 2.85% in Recall, and 2.89% in F1 Score. The model also demonstrated enhanced attention to graphite ore features. Compared with some mainstream CNN algorithms, our method offers significant advantages in accuracy and robustness, making it highly suitable for graphite ore grade recognition. |
| format | Article |
| id | doaj-art-95289a864f3947089c7dba37e3f5585f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-95289a864f3947089c7dba37e3f5585f2025-08-20T03:40:22ZengIEEEIEEE Access2169-35362025-01-0113480074801810.1109/ACCESS.2025.355042310921646A Graphite Ore Grade Recognition Method Based on Improved Inception-ResNet-v2 ModelXueyu Huang0Renjie Pan1https://orcid.org/0009-0004-4643-4717Jionghui Wang2School of Software Engineering, Jiangxi University of Science and Technology, Nanchang, ChinaSchool of Software Engineering, Jiangxi University of Science and Technology, Nanchang, ChinaMinmetals Exploration and Development Company Ltd., Beijing, ChinaWith the rapid advancement of technology, intelligent identification of graphite ore grade in graphite mines has emerged as an essential requirement. To address the variability and low timeliness of traditional manual methods and the limited accuracy of deep learning due to image complexity and feature similarity, we propose an improved Inception-ResNet-v2 model for graphite ore grade recognition. Key improvements include: 1) To enhance the extraction of global feature information from graphite mine data, a global average pooling branch is incorporated into the Inception-resnet architecture. 2) Incorporating a <inline-formula> <tex-math notation="LaTeX">$1\times 1$ </tex-math></inline-formula> convolutional layer at the tail of the model to control channel dimensions and employing the LeakyReLU activation function to address the limitations of the ReLU activation function. 3) Designing an LDP-Conv structure to replace certain <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> convolutions and incorporating a channel attention mechanism to improve feature capture. 4) Optimizing the Stem module to expand the early-stage receptive field and reconstructing the network architecture. Experiments on the self-made graphite ore dataset showed improvements of 2.77% in Accuracy, 2.96% in Precision, 2.85% in Recall, and 2.89% in F1 Score. The model also demonstrated enhanced attention to graphite ore features. Compared with some mainstream CNN algorithms, our method offers significant advantages in accuracy and robustness, making it highly suitable for graphite ore grade recognition.https://ieeexplore.ieee.org/document/10921646/Inception-ResNet-v2graphite ore grade recognitionLeakyReLUglobal average pooling branchLDP-Convmodel restructure |
| spellingShingle | Xueyu Huang Renjie Pan Jionghui Wang A Graphite Ore Grade Recognition Method Based on Improved Inception-ResNet-v2 Model IEEE Access Inception-ResNet-v2 graphite ore grade recognition LeakyReLU global average pooling branch LDP-Conv model restructure |
| title | A Graphite Ore Grade Recognition Method Based on Improved Inception-ResNet-v2 Model |
| title_full | A Graphite Ore Grade Recognition Method Based on Improved Inception-ResNet-v2 Model |
| title_fullStr | A Graphite Ore Grade Recognition Method Based on Improved Inception-ResNet-v2 Model |
| title_full_unstemmed | A Graphite Ore Grade Recognition Method Based on Improved Inception-ResNet-v2 Model |
| title_short | A Graphite Ore Grade Recognition Method Based on Improved Inception-ResNet-v2 Model |
| title_sort | graphite ore grade recognition method based on improved inception resnet v2 model |
| topic | Inception-ResNet-v2 graphite ore grade recognition LeakyReLU global average pooling branch LDP-Conv model restructure |
| url | https://ieeexplore.ieee.org/document/10921646/ |
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