Aiming at the problem of low classification accuracy of surface defects of lithium battery pole pieces by traditional classification methods, an image classification algorithm for surface defects of lithium battery pole piece based on deep learning is proposed in this paper. Firstly, Wavelet Threshold and Histogram Equalization are used to preprocess the detect image to weaken influence of noise in non-defect regions and enhance defect features. Secondly, a VGG-InceptionV2 network with better performance is proposed by adding InceptionV2 structure to the improved VGG network structure. Then the original data set is expanded by rotating, flipping and contrast adjustment, and the optimal value of the model hyperparameters is determined by experiments. Finally, the model in this paper is compared with VGG16 and GoogLeNet to realize the recognition of defect types. The results show that the accuracy rate of the model in this paper for the surface pole piece defects of lithium batteries is 98.75%, and the model parameters is only 1.7M, which has certain significance for the classification of lithium battery surface pole piece defects in industry.

An automatic defect classification system (ADC) for use in visual inspection of semiconductor wafers is introduced. The methods of extracting the defect features based on the human experts' knowledge, with their correlations with the defect classes are elucidated. As for the classifier, Hyperellipsoid Clustering Network (HCN) which is a layered network model employing second order discrimination borders in the feature space, is introduced. In the experiments using a collection of defect images, the HCNs are compared with the conventional multilayer perceptron networks. There, it is shown that the HCN's adaptive hyperellipsoidal discrimination borders are more suited for the problem. Also, the cluster encapsulation by the hyperellipsoidal border enables to determine rejection classes, which is also desirable when the system will be in actual use. The HCN with rejection achieves, an overall classification rate of 75% with an error rate of 18%, which can be considered equivalent to those of the human experts.