Identifying Graphite Purity by Weighted Fusion Method

Abstract

The purity of graphite often affects its application in different fields. In view of the low efficiency of manual recognition and the omission of features extracted by single convolution neural network, this paper proposes a method for identifying graphite purity using a multi-model weighted fusion mechanism. The ideas suggested in this paper are as follows. On the self-built small sample data set, offline expansion and online enhancement are carried out to improve the generalization ability of the model and reduce the overfitting problem of deep convolution neural networks. Combined with transfer learning, a dual-channel convolution neural network is constructed using the optimized Alex Krizhevsky Net (AlexNet) and Alex Krizhevsky Net 50 (AlexNet50) to extract the deep features of the graphite image. After the weighted fusion of the two features, the Softmax classifier is used for classification. Experimental results show that recognition accuracy after weighted fusion is better than that of single network, reaching 97.94%. At the same time, the stability of the model is enhanced, and convergence speed is accelerated, which proves the feasibility and effectiveness of the proposed method.