Abstract: Aluminum alloy casting has been widely used in automobile, aircraft and other important industries, where its quality directly affects the safety of mechanical parts. Aiming at the diversification and minuteness of defects in the surface and interior of the X-ray images of aluminum alloy casting, a casting defect detection method based on local and global features was proposed. Firstly, an efficient channel attention module efficient channel attention is fused with the classical network resnet-50 to form a new basic convolutional neural network, which serves as the backbone for constructing a double-branch network model. Then, a detailed information location and extraction (DILE) module is proposed, which located the local area containing rich discriminant information. Finally, combining the local image obtained by DILE with the original image as the input to the network, a double branch network model integrating local and global features is constructed. The global region learning aids in extracting meaningful subtle information in complex background, while the learning of local region further improves the classification effectiveness. The method was tested and trained on an X-ray image data set of real automobile castings, achieving a test set accuracy of 98.3%. Experimental results show that this method is more effective than conventional methods.

Key words: deep learning, non-destructive testing of casting defects, attention mechanism, X-ray image