Defect detection and segmentation in X-Ray images of magnesium alloy castings using the Detectron2 framework
This work addresses quality control challenges in manufacturing by reducing operator subjectivity and variability, though it is incremental as it uses an existing method on new data.
The paper tackled defect detection and segmentation in X-Ray images of magnesium alloy castings by applying the Detectron2 framework, achieving automated identification of internal and superficial defects to improve quality control in automotive parts.
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