Crack Segmentation for High-Speed Imaging: Detection of Fractures in Thermally Toughened Glass


  • Henrik Riedel Technical University of Darmstadt
  • Leon Bohmann Technical University of Darmstadt
  • Frank Bagusat Fraunhofer Ernst Mach Institut
  • Martin Sauer Fraunhofer Ernst Mach Institut
  • Miriam Schuster Technical University of Darmstadt
  • Matthias Seel Technical University of Darmstadt


Fracture morphology characterization in broken glass panes is crucial for designing laminated safety glass (LSG) in civil engineering. Verifying completely broken LSG systems requires destructive sampling, increasing costs and hindering development. Therefore, to determine the residual load-bearing capacity, the link between the pre-fracture characteristics and the fracture morphology must be
known. However, when the crack propagation needs to be directly captured with high-speed imaging, conventional methods are no longer sufficient for detecting cracks. To enable such investigations, we propose a novel machine learning framework for crack segmentation in high-speed imaging that addresses the complexity of glass fracture and minimises the required labour costs. In this study, the crack propagation of a sample was recorded and analysed at 2,000,000 images per second. The results showcase accuracies surpassing 97% while requiring only two labeled images for training, thus streamlining practical implementation. Furthermore, we show the method's robustness to the extent that hyperparameter tuning becomes unnecessary. Instead, we offer guidelines for selecting the most crucial hyperparameters depending on the problem. Our method offers a promising approach for non-
linear temporal interpolation of noisy images, with implications for various applications extending beyond glass fracture analysis.





Experimental & Numerical Investigations