Casting defect detection in X-ray images using convolutional neural networks and attention-guided data augmentation

Aluminum 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.

Automated summary

This paper proposes a weakly-supervised Convolutional Neural Network model based on X-ray images to recognize casting defects. The model achieves high test accuracy and recall through attention maps and data augmentation methods, demonstrating accuracy and robustness. The efficiency of the proposed approach is verified by comparing with existing methods and ablation experiments.