Classification method of coal and coal gangue based on AlexNet-SN network
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Abstract
The existing coal gangue separation methods mainly identify coal gangue based on manual design features, but the feature extraction process is complex and the accuracy is low.With the rapid development of artificial intelligence technology, intelligent gangue separation has become an important research direction to solve the problem of coal gangue sorting.In order to improve the classification accuracy of coal and coal gangue, an improved coal gangue sorting method based on AlexNet network and style migration technology is proposed in this paper.Selecting 3×3 instead of the larger convolution kernel in the first few layers of the original AlexNet network, and BN layer is used to replace LRN and Dropout, and using the style migration data enhancement method to improve the diversity of coal and coal gangue data sets.The results show that compared with the original AlexNet network, the accuracy of this method is improved by 1.8% and the loss rate is reduced by 2.0%.This method can not only meet the requirements of real-time detection of coal and coal gangue, but also has higher recognition accuracy, and can be effectively applied to coal gangue recognition.
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