Welding Defect Classification Based on Lightweight CNN

Abstract

The welding defect classification method based on deep learning often faces problems such as insufficient training data and complex model structures, which affect its real-time performance. Therefore, a welding defect classification method based on lightweight convolutional neural network (CNN) is proposed. The problems of insufficient and unbalanced welding defect image are solved by using the generative adversarial networks (GANs) data augmentation method. A lightweight CNN model is developed, which reduces the structural parameters under the premise of ensuring classification accuracy. The feature data of each convolution layer are visualized to verify the feasibility of the model and improve the interpretability of the model. By comparing the accuracy and real-time performance with other lightweight models, the excellent performance of the proposed model in welding defect classification is verified. Additionally, our model achieves 98.25% accuracy on the MNIST dataset.