Fine segmentation method of blast heap block in open pit mine based on point rendering and multi-branch fusion

In modern mineral exploitation, blasting cost accounts for a large part of the cost of the whole mineral exploitation, which makes the control of blasting effect crucial in the whole mineral exploitation process. The control of blasting effect is directly related to blasting parameters, which requires the collection of real field data to guide the optimization of blasting parameters. In order to solve the current problems of low accuracy, poor real-time performance and weak generalization performance, a fine segmentation method of blasting pile block under the deep learning framework(Point+S Deeplabv3+)is proposed based on the need of blasting parameter optimization. Firstly, the model introduces a multi-branch separable attention mechanism in the feature extraction part of the backbone network blast pile block degree identification in mining sites, a fine segmentation method for blast pile blocks(Point+S Deeplabv3+) under the deep learning framework to learn the weight features between different channels and combine them, which improves the problem of lack of cross-channel interaction when extracting features. In the decoding stage, the point rendering module is used to gradually splice the low-level semantic features and high-level semantic features corresponding to each point by iterating the features of the selected points in different scale feature maps, which solves the problem of losing a large amount of semantic information during sampling, which makes the accuracy of edge and small target segmentation reduced. Finally, the dynamic learning rate adjustment strategy is used to accelerate the convergence speed of the model. The experimental results show that the MPA and MIoU of the Point+S Deeplabv3+ -based model reach 94.36% and 89.04%, respectively. Comparing with the mainstream semantic segmentation networks, such as FCN, UNet, PSPnet and Deeplabv3+, the MPA and MIoU of the Point+S Deeplabv3+-based model are improved by 3.04%, 4.44%, 2.79%, 1.52% and 2.95%, 4.36%, 3.17%, 1.88%, with better overall performance, especially for the segmentation of edges and small targets. Therefore, the segmentation model based on the Point+S Deeplabv3+ provides a real-time convenient and reliable theoretical basis for the optimal data acquisition of blasting parameters in the blasting field environment.