X-ray Ore Image Classification Method Based on Improved ACGAN Data Augmentation in Small Samples
Aiming at the problems of overfitting and low classification accuracy due to the scarcity of ore samples in industrial applications of deep learning models for online ore classification,a method combining X-ray transmission imaging technology for ore data augmentation and classification is proposed.The method is based on an improved auxiliary classifier generative adversarial network(Enhance-based Classification ACGAN-gp,EC-ACGAN-gp),which uses convolutional and continuous residual blocks to construct the network structures of the discriminator and generator.An attention mechanism is introduced to capture detailed ore features and generate high-quality samples to expand the original dataset.Simultaneously,the Wasserstein distance function with gradient penalty is used to reconstruct the classification loss function,achieving improved stability of adversarial training and avoiding mode collapse.Finally,an auxiliary classifier is utilized to reconstruct label information for ore sample category prediction.The research results show that the proposed method can accurately predict the ore grade classification,with an accuracy of up to 89.62%,which is 3.98%higher than traditional methods.The model-generated ore samples demonstrate good generalization performance,significantly improving the generalization of small sample datasets.When tested on SVM,LeNet5,VGGNet,and ResNet,the accuracy is increased by 2.83%,2.36%,1.89%,and 3.74%,respectively.This method can further be used to enhance the performance of other classification models in ore grade prediction.
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