In order to solve the problem of high difficulty of defect classification recognition of X-ray weld seam

low efficient and complicated that traditional algorithms is dense convolutional network（ DenseNet） algorithm based on deep learning is introduced

and the data are dynamically enhanced. Dense Net network algorithm breaks away from the stereotyped thinking that needs to deepen network layers and widen network structure to improve performance in machine learning algorithm. Through feature reuse and bypass setting

the detection and recognition of weld defects can be realized. Under the same data set and training steps. compared with LS-SVM and LeNet algorithm of convolution neural network

DenseNet network improves the generalization ability and recognition accuracy of the model

and the recognition accuracy of weld defects can reach 98. 969 %.