In the automotive industry, light-alloy aluminum castings are an important element for determining roadworthiness. X-ray testing with computer vision is used during automated inspections of aluminum castings to identify defects inside of the test object that are not visible to the naked eye. In this article, we evaluate eight state-of-the-art deep object detection methods (based on YOLO, RetinaNet, and EfficientDet) that are used to detect aluminum casting defects. We propose a training strategy that uses a low number of defect-free X-ray images of castings with superimposition of simulated defects (avoiding manual annotations). The proposed solution is simple, effective, and fast. In our experiments, the YOLOv5s object detector was trained in just 2.5 h, and the performance achieved on the testing dataset (with only real defects) was very high (average precision was 0.90 and the \(F_1\) factor was 0.91). This method can process 90 X-ray images per second, i.e. ,this solution can be used to help human operators conduct real-time inspections. The code and datasets used in this paper have been uploaded to a public repository for future studies. It is clear that deep learning-based methods will be used more by the aluminum castings industry in the coming years due to their high level of effectiveness. This paper offers an academic contribution to such efforts.

在汽车工业中，轻合金铝铸件是确定耐用性的重要元素。在自动检查铝铸件期间，使用具有计算机视觉的X射线测试来识别测试对象内部肉眼看不到的缺陷。在本文中，我们评估了用于检测铝铸件缺陷的八种最先进的深层物体检测方法（基于YOLO，RetinaNet和EfficientDet）。我们提出了一种训练策略，该策略使用少量铸件的无缺陷X射线图像并叠加模拟缺陷（避免使用人工注释）。所提出的解决方案简单，有效且快速。在我们的实验中，仅2.5小时就对YOLOv5s目标检测器进行了训练，\（F_1 \）系数为0.91）。该方法每秒可处理90张X射线图像，即该解决方案可用于帮助操作员进行实时检查。本文中使用的代码和数据集已上载到公共存储库中，以备将来研究。显然，基于深度学习的方法由于其高水平的有效性，将在未来几年中被铝铸件行业广泛使用。本文为此类工作提供了学术上的贡献。