Research on unloading drill-rod action identification in coal mine water exploratio

Abstract

In view of low efficiency and error prone problems in the way that supervisors of underground water exploration operation realize monitoring of unloading drill-rod operation by watching video, 3D convolutional neural network (3DCNN) model is proposed to identify unloading drill-rod action in water exploration operation. In 3DCNN model, 3D convolution layer is used to automatically extract action features, 3D pooling layer is used to reduce dimension of motion features, softmax classification is used to identify unloading dirll-rod action, and batch normalization layer is used to improve convergence speed and identification accuracy of the model. When the 3DCNN model is used to identify unloading drill-rod action, firstly, the data set is preprocessed, and several frames of images are extracted from each video as representatives of an action, and the resolution is reduced; secondly, the training set is used to train the 3DCNN model, and the trained weight file is saved; finally, the trained 3DCNN model is used to test the test set, and the classification results are obtained. The experimental results show that when the number of sampling frames is 10, the resolution is 32×32, and the learning rate is 0.000 1, the highest recognition accuracy of the model can reach 98.86%.

Keywords

• coal mine water control
• coal mine water exploration
• unloading drill-rod action identification
• 3d convolutional neural network
• 3dcnn
• batch normalizatio