Modern Mining ›› 2022, Vol. 38 ›› Issue (08): 248-.
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LI Hui1 LIU Gui2 YUAN Hang2 WANG Yuchen2
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Abstract: Accurate classification and identification of coal gangue is an important problem to be solved in the safe and precise mining of coal mines. Residual network shows great advantages in image clas⁃ sification tasks. Using residual network and overcoming its shortcomings in feature extraction,a mine image classification model is proposed. The model combines multi-scale idea and repeated attention method,and also adds ResNet network feature extraction method to the model. In addition,the jump connection is add⁃ ed to the model,which can reduce the calculation amount of the model. Based on the real experimental da⁃ ta,the accuracy of this model is improved by 3 % compared with other classification models.
Key words: coal gangue, image classification, repeated attention, feature extraction
LI Hui, LIU Gui, YUAN Hang, WANG Yuchen. Mine Image Classification Based on Multi-scale and Repetitive Attention Mechanism[J]. Modern Mining, 2022, 38(08): 248-.
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https://www.xdky.net/EN/Y2022/V38/I08/248