Semantic Segmentation Method of Weld Defects Using Multilevel and Multi-Scale Neural Network Self-Search
To further improve the accuracy of semantic segmentation of low-quality X-ray images of weld de-fects and reduce the subjective impact and time-consuming of artificial design networks,a semantic segmenta-tion method of weld defects using multilevel and multi-scale neural network self-search is proposed.Through the design of multi-scale lightweight candidate operations,channel attention mechanism and multi-level dy-namic network,the expression ability of the network to extract defect features of low-quality images is im-proved from different dimensions;at the same time,through the exploration of the correlation between the recognition performance of the early and final stages of network training,a gradually fast neural architecture search method using fixed sampling to gradually determine the optimal candidate operation is proposed.In the architecture search phase,483 X-ray weld defect images collected and annotated by oneself were used for ar-chitecture optimization through random cropping,rotation,translation,and other data augmentation operations.Finally,a semantic segmentation network for weld defects was automatically constructed at a lower search cost.Experiments show that using the above ideas for semantic segmentation of X-ray weld defects,the final mloU index reaches 49.23％,which is higher than 45.41％of the artificial design network and 28.86％of the direct use of model transfer.The self-search speed and segmentation effect of the network are significantly im-proved.
万方数据
维普
