In order to achieve rapid acquisition of open-pit mine road network information by deep learning image processing technology,the article constructs two typical network model frameworks,Deeplabv3+and PSPNet,selects ResNet with different layers as the backbone network to construct an open-pit mine road information extraction model.Through horizontal comparison of the framework model and vertical comparison of ResNet with different layers under the same framework,the article studies the optimal allocation problem between model recognition accuracy and speed and ResNet layers.The results show that taking into account both the training time and recognition performance of the model,the Deeplabv3+network framework has problems with road information loss,inability to recognize,or large errors in recognizing image edge areas,and is not suitable for image processing of road information in open-pit mines.The PSPNet network framework can accurately identify multiple types of roads with different features in complex mining environments,and the backbone network ResNet can maintain a layer count of 50 to meet the recognition accuracy while ensuring recognition speed and efficiency.
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