The X-ray detection method is widely used in the detection of weld internal defects， but in the detection process， product quality is rated mainly by manually viewing X-ray images. This detection method is inefficient， and requires rich experience of operators， which is difficult to meet the needs of batch detection. It is thus urgent to improve the detection efficiency with the help of the convolution neural network. This paper proposes a CC-ResNet model with composite convolution layer network structure， which combines convolution cores of different scales into one layer of convolution， replaces the original single convolution core structure， and optimizes the loss function in the process of model training. A data set is established by using the weld X-ray images of workshop products. Experiment on CC-ResNet network model shows that the average recall rate and accuracy are 98.52% and 95.23%， respectively. Compared with other network models， the model proposed can improve the defect detection ability， providing an algorithm basis for intelligent and batch detection of weld defects in the workshop.
LIU Huan
,
LIU Xiaojia
,
WANG Yufei
,
WANG Ning
,
CAO Lijun
. Weld defect classification technology based on compound convolution neural network structure[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022
, 43(S1)
: 726928
-726928
.
DOI: 10.7527/S1000-6893.2022.26928
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