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• W4387444329 abstract "To improve the recognition accuracy of coal and gangue images while avoiding complex model structures, this paper builds an improved convolutional neural network based on residual networks and applies it to the study of coal and gangue image recognition. Firstly, we perform various image enhancement processes on the acquired image data to enrich the experimental samples. Secondly, we analyze the features of the Ghost module and coordinate attention mechanism (CA), combining their advantages to propose coordinate attention augmenting the Ghost module. Then, the proposed module is used instead of convolution operation to build our recognition model according to the underlying network structure. Through comparative analysis, the number of parameters and floating point of operations (FLOPs) of the model are decreased by 46.9% and 46.29%, respectively, in comparison to the basic model, and the prediction accuracy can reach 97.97%. Finally, the model is contrasted with the commonly used convolutional neural networks further to confirm its efficacy. The results demonstrate that the accuracy of the proposed model is increased by up to 2.19% compared to the mainstream networks and that the model's parameters and FLOPs count have been decreased by 89.82% and 93.6%, respectively. The average predicted time for a single picture of coal and gangue is 9.76ms. Therefore, this study has high recognition accuracy and fast inference efficiency for coal and gangue, which can provide effective help for the separation of coal and gangue and has certain practical application value." @default.
• W4387444329 created "2023-10-10" @default.
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• W4387444329 date "2023-07-27" @default.
• W4387444329 modified "2023-10-11" @default.
• W4387444329 title "Research on Image Recognition of Coal and Gangue Based on Improved ResNet" @default.
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• W4387444329 doi "https://doi.org/10.1109/icivc58118.2023.10270713" @default.
• W4387444329 hasPublicationYear "2023" @default.
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