Coal dust image recognition based on improved VGG convolution network

In views of the problems of particle contour overlap and unclear texture detection of coal dust explosion, this paper proposed a method based on improved deep learning vgg-16 convolutional neural network model to obtain the feature information of particle image. Based on the vgg-16 network model, the SELayer is added after sampling under the first two convolutional layers to compress and extract the deep features of the particle image. The original SoftMax classifier was replaced by a binary classifier to optimize the model parameter structure. The weight parameters of convolution layer and pooling layer in the pre-training model were shared by micro-migration learning to speed up the operation. Samples were randomly selected from the constructed coal dust image as training set and test set to test the performance indexes of the model. The experimental results show that the proposed method has 2% promoted of recognition accuracy to the conventional methods, and achieves a lower loss value, which can meet the detection requirements of coal dust particle image.