Research on Weld Defect Recognition Technology Based on Improved VGG-16 Network Structure
Welding technology is widely used in multiple fields,and the automatic detection of weld defects has become a research hotspot in recent years.In this paper,aiming at the X-ray images of aluminum alloy fusion welding seams,a new network structure called SC-VGG is proposed,using the VGG-16 convolutional neural network as the basic network.This structure replaces the traditional single convolutional layer with a multi-scale synthetic convolutional layer and optimizes the loss function in the training process,making the network more focused on accurate prediction of weld defect types.Experimental results show that the SC-VGG network structure exhibits good convergence during the training process.Compared with other networks,the SC-VGG network performs excellently in extracting weld defect features,with an average accuracy and recall rate reaching 95.86%and 98.33%respectively,providing algorithm support for the automatic classification of weld defects.
NETL
NSTL
万方数据
