The radiographic inspection plays a crucial role in ensuring the casting quality for improving the service life under harsh environments. However, due to the low-contrast between the defects and the image background, the random spatial position distribution, random shapes and aspect ratios of the defects, the development of an accurate defect automatic detection system is still challenging. To address these issues, this paper proposes a novel framework for low-contrast and random multi-scale casting defect detection, which is referred to as adaptive global dynamic detection transformer (AGD-DETR). A novel defect-aware data augmentation method is first proposed to adaptively highlight the feature of the low-contrast defect boundary. A multi-attentional pyramid feature refinement (MPFR) module is then established to refine and fuse the multi-scale defect features of random sizes. Afterwards, a novel global dynamic receptive fusion-transformer (GDRF-Transformer) detection scheme is designed to perform the global perception and feature dynamic extraction of complex internal casting defects. It includes 4D-anchor query and cross-layer box update strategy, query rectification by prior information of defect aspect ratio, and global adaptive-feed forward network (GA-FFN). A dataset comprising turbine blade casting defect radiographic (TBCDR) images, is used to demonstrate the high efficiency of the proposed AGD-DETR. The obtained results show that the proposed method can accurately capture the spatial position distributions and complex defect shapes. Furthermore, it outperforms existing state-of-the-art defect detection methods.

中文翻译：

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通过自适应全局动态检测变压器进行低对比度和随机多尺度叶片铸造缺陷检测的新颖框架


射线检测对于保证铸件质量、提高恶劣环境下的使用寿命起着至关重要的作用。然而，由于缺陷与图像背景之间的低对比度、缺陷的随机空间位置分布、随机形状和长宽比，开发精确的缺陷自动检测系统仍然具有挑战性。为了解决这些问题，本文提出了一种低对比度和随机多尺度铸造缺陷检测的新框架，称为自适应全局动态检测变压器（AGD-DETR）。首先提出了一种新颖的缺陷感知数据增强方法来自适应突出低对比度缺陷边界的特征。然后建立多注意金字塔特征细化（MPFR）模块来细化和融合随机大小的多尺度缺陷特征。随后，设计了一种新颖的全局动态接收融合变压器（GDRF-Transformer）检测方案来执行复杂内部铸造缺陷的全局感知和特征动态提取。它包括4D锚查询和跨层框更新策略、通过缺陷长宽比先验信息进行查询校正以及全局自适应前馈网络（GA-FFN）。包含涡轮叶片铸造缺陷射线照相 (TBCDR) 图像的数据集用于证明所提出的 AGD-DETR 的高效率。所得结果表明，该方法能够准确捕捉空间位置分布和复杂缺陷形状。此外，它优于现有的最先进的缺陷检测方法。