Research on weld defect image classification based on convolutional neural network

In order to effectively classify the weld defects and judge the grade of the welding quality, a multi-scale squeeze-and-excitation network model (SINet) was proposed to improve the traditional convolutional neural network. Combined 4 groups of 3×3 convolutional modules in series with Inception module and squeeze-and-excitation block (SE block). By means of the multi-scale squeeze-and-excitation module (SI module), the multi-scale fusion and the feature re-calibration were carried out of the features in convolutional layer to improve the classification accuracy, and the global average pooling layer was used instead of the fully connected layer to reduce the model parameters. In addition, considering the influence of the unbalance in the number of weld defects on the accuracy, a deep convolutional adversarial generation network (DCGAN) method was used to balance the data set, and the validity of the model was verified on the data set. Compared with the traditional convolutional neural network, this model has good performance with an accuracy rate on the test of 96.77%, and the number of the model parameters is also greatly reduced. The results show that this method can effectively classify the weld defect images.