Research on Feature Extraction of Rock Mass Discontinuities Based on Deep Learning
Blasting is a necessary part of underground metal mining,reasonable blasting parameters can improve the overall safety and production efficiency of underground mines.However,in the actual production process,if the original parameters are still used when the geological conditions of the mine change,there will be the problem of inaccurate blasting prediction and poor blasting effect due to the solidification of parameters.In order to achieve the expected blasting effect,it is necessary to obtain the relevant parameters of blasting.In this paper,the feature extraction of rock structure surface is studied based on deep learning technology,and by comparing and selecting different algorithms and parameters,the MobilenetV3 algorithm is finally used as the infrastructure and optimized to improve the correctness of the algorithm by 14.07%,reaching 96.88%.
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