Aluminium alloy castings have a high utilization rate in the automotive industry, and its quality directly affects the safety performance of the mechanical components. Hence, casting quality management is vital during the casting production process. This paper presents a weakly-supervised Convolutional Neural Network model to recognize defects based on casting X-ray images. These images are divided into two classes including defective and non-defective. Firstly, attention maps are generated to represent the defective parts by weakly-supervised learning for each image. Then mutual-channel loss combined with the cross-entropy loss function encourage the network to focus on discriminative features. Simultaneously, a novel data-augmentation methods guided by these attention maps is proposed to enlarge the dataset. The test accuracy achieves 95.5%, and the recall is 96.0%, which means our model is accurate and robust. The efficiency of the proposed approach is verified by comparing the state-of-art approaches and the ablation experiments.

铝合金铸件在汽车工业中具有很高的利用率，其质量直接影响机械部件的安全性能。因此，在铸件生产过程中，铸件质量管理至关重要。本文提出了一种弱监督的卷积神经网络模型，用于基于铸造X射线图像识别缺陷。这些图像分为有缺陷的和无缺陷的两类。首先，通过对每个图像进行弱监督学习来生成注意力图，以表示缺陷部位。然后，相互信道损失与交叉熵损失函数相结合，促使网络将注意力集中在区分特征上。同时，提出了一种以这些注意图为指导的新的数据增强方法来扩大数据集。测试精度达到95.5％，召回率为96.0％，这意味着我们的模型准确且可靠。通过比较现有技术方法和消融实验，验证了所提方法的效率。