关键词: 金属缺陷识别, 深度学习, 卷积神经网络, Transformer模型, 多头注意力

Abstract: Aiming at the problems of large amount of parameters and calculation in the field of metal defect recognition， a metal defect recognition method was proposed based on shallow CNN and Transformer model. The shallow CNN was used to learn the local information and position information of the images， and the Transformer was used to learn the global information of the images. At the same time， the channel attention module SE was introduced to pay attention to the important feature channels to realize the defect image recognition. The effectiveness of this method was verified by introducing the open defect data set， and the universality of this method was verified by using the self-built defect ultrasonic data set. The experimental results show that the proposed method has strong universality and may effectively identify metal defect images on small and medium-sized data sets. 

Key words: metal defect identification, deep learning, convolutional neural network（CNN）, Transformer model, multi-head attention