铝合金铸件已经广泛应用于汽车、飞机等重要工业，其质量直接影响到机械零部件的安全使用。针对铝合金铸件的X射线图像表面和内部缺陷多样化和细微问题，提出了一种融合局部和全局特征的X射线图像铸造缺陷检测方法。首先，将高效通道注意力模块和经典网络ResNet-50进行融合构成新的基础卷积神经网络，以新的网络为骨干构建双分支网络模型。然后，提出了细节信息定位提取（detailed information location and extraction,DILE）模块，该模块定位到包含丰富判别性信息的局部区域。最后，将由DILE得到的局部图像结合原始图像作为网络的输入，构建了一个融合局部和全局特征的双分支网络模型。对全局区域的学习有助于在复杂背景下提取有意义的细微信息，对局部区域的学习可以进一步提高分类效果。该方法在真实汽车铸件的X射线图像数据集上进行测试训练，测试集准确率达98.3%。实验结果表明，该方法相较于其他常规方法更有效。
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.
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