关键词: 缺陷检测, 深度学习, 随机子空间, 级联残差网络, 小样本

Abstract:

Aiming at the small sample problem of surface defect detection method based on deep learning, a defect detection method combining random subspace and cascade residual network（RSM-MTResNet） is proposed. Firstly, the defect data is decomposed into multiple random subspaces, then the residual network is constructed on each subspace, and then the fusion features are obtained by cascades of multiple residual networks. Experiments are carried out on the NEU surface defect data set, and confusion matrix and F1 values are used to evaluate the model performance. The result shows that the classification accuracy of the method in this paper is 97.66%, 14.5% higher than that of the traditional CNN method, and F1 values are all improved by more than 10.0%, which proves that the method can not only solve the problem of small samples to a certain extent, but also achieve high recognition performance.

Key words: defect detection, deep learning, random subspace, cascade residual network, small sample