Nondestructive testing using X-ray imaging has been widely adopted in the defect detection of casting parts for quality management. Deep learning has been proved to be an effective way to detect defects in X-ray images. In this work, Feature Pyramid Network (FPN) which has been utilized broadly in many applications is adopted as our baseline. In FPN, there mainly exits two issues: firstly, down sampling operation in Convolutional Neural Network is often utilized to enhance the perception field, causing the loss of location information in feature maps, and secondly, there exists feature imbalance in feature maps and proposals. DetNet and Path Aggregation Network are adopted to solve the two shortages. To further improve the recall rate, soft Non-Maximum Suppression (soft-NMS) is adopted to remain more proposals that have high classification confidence. Defects in X-ray images of casting parts are provided with low semantic information, causing the different instances between detection results and annotations in the same area. We propose soft Intersection Over Union (soft-IOU) criterion which could evaluate several results or ground truths in the near area, making it more accurate to evaluate detection results. The experimental results demonstrate that the three proposed strategies have better performance than the baseline for our dataset.

使用X射线成像的无损检测已广泛用于铸件缺陷检测以进行质量管理。深度学习已被证明是检测X射线图像中缺陷的有效方法。在这项工作中，采用了在许多应用程序中广泛使用的功能金字塔网络（FPN）作为我们的基准。在FPN中，主要存在两个问题：首先，卷积神经网络中的下采样操作经常被用来增强感知场，从而导致特征图中位置信息的丢失；其次，在特征图中和提议中存在特征不平衡。为了解决这两个不足，采用了DetNet和Path Aggregation Network。为了进一步提高召回率，采用软非最大抑制（soft-NMS）来保留更多具有高分类置信度的投标。铸造零件的X射线图像中的缺陷提供的语义信息很少，从而导致同一区域中的检测结果和注释之间的实例不同。我们提出了“软联合交叉口”（soft-IOU）准则，该准则可以评估附近地区的多个结果或地面真实情况，从而更加准确地评估检测结果。实验结果表明，所提出的三种策略比我们的数据集的基线具有更好的性能。我们提出了“软联合交叉口”（soft-IOU）准则，该准则可以评估附近地区的多个结果或地面真实情况，从而更加准确地评估检测结果。实验结果表明，所提出的三种策略比我们的数据集的基线具有更好的性能。我们提出了“软联合交叉口”（soft-IOU）准则，该准则可以评估附近地区的多个结果或地面真实情况，从而更加准确地评估检测结果。实验结果表明，所提出的三种策略比我们的数据集的基线具有更好的性能。