论文标题
使用检测框架的镁合金铸件的X射线图像中的缺陷检测和分割
Defect detection and segmentation in X-Ray images of magnesium alloy castings using the Detectron2 framework
论文作者
论文摘要
已经出现了新的生产技术，这些技术已成为有可能具有更复杂形状的金属零件，从而使质量控制过程变得更加困难。这意味着视觉和肤浅的分析变得更加效率更低。最重要的是，也无法检测到这些部分可能存在的内部缺陷。 X射线图像的使用使此过程变得更加容易，不仅可以以更简单的方式检测表面缺陷，而且还可以检测焊接或铸造缺陷，这可能代表了金属部件物理完整性的严重危害。另一方面，使用自动分割方法检测缺陷将有助于减少缺陷检测对出厂操作员主观性及其时间依赖性变异性的依赖性。本文的目的是基于detectron2应用深度学习系统，该系统是应用于图像中对象检测和分割的最新库，以识别和分割这些缺陷，主要是从汽车部件获得的X射线图像
New production techniques have emerged that have made it possible to produce metal parts with more complex shapes, making the quality control process more difficult. This implies that the visual and superficial analysis has become even more inefficient. On top of that, it is also not possible to detect internal defects that these parts could have. The use of X-Ray images has made this process much easier, allowing not only to detect superficial defects in a much simpler way, but also to detect welding or casting defects that could represent a serious hazard for the physical integrity of the metal parts. On the other hand, the use of an automatic segmentation approach for detecting defects would help diminish the dependence of defect detection on the subjectivity of the factory operators and their time dependence variability. The aim of this paper is to apply a deep learning system based on Detectron2, a state-of-the-art library applied to object detection and segmentation in images, for the identification and segmentation of these defects on X-Ray images obtained mainly from automotive parts
