Abstract An automated and reliable non-destructive testing (NDT) system for aluminium alloy casting is the key to guaranteeing the quality of products and reducing production costs. In order to achieve accurate detection with a digital radiography (DR) based NDT system, we propose a new deep model to detect the subtle defects of aluminium alloy casting on the X-ray image. Specifically, the proposed model is composed of two subnetworks: a general feature network (GFN), and a subtle feature network (SFN). Furthermore, the self-attention mechanism is modeled as a self-attention guided module (SGM) embedded in the SFN. SGM enhances the capability of the model to extract subtle features against a complex background. The effectiveness of the proposed model is validated on the actual X-ray images acquired in the casting process. The experimental results demonstrate that the proposed model has more satisfactory detection performances in comparison with classical deep learning models.

中文翻译：

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X射线图像上铝合金铸件缺陷检测的自注意力引导模型

摘要 自动化、可靠的铝合金铸件无损检测系统是保证产品质量、降低生产成本的关键。为了使用基于数字射线照相 (DR) 的 NDT 系统实现准确检测，我们提出了一种新的深度模型来检测 X 射线图像上铝合金铸件的细微缺陷。具体来说，所提出的模型由两个子网络组成：通用特征网络（GFN）和微妙特征网络（SFN）。此外，自注意力机制被建模为嵌入在 SFN 中的自注意力引导模块 (SGM)。SGM 增强了模型在复杂背景下提取细微特征的能力。在铸造过程中获得的实际 X 射线图像上验证了所提出模型的有效性。