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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Schuecker, Clara
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (7/7 displayed)
- 2023Comparing crack density and dissipated energy as measures for off-axis damage in composite laminatescitations
- 2022Efficient prediction of crack initiation from arbitrary 2D notchescitations
- 2022Improved concept for iterative crack propagation using configurational forces for targeted angle correctioncitations
- 2022Efficient Finite Element Modeling of Steel Cables in Reinforced Rubbercitations
- 2021CrackDect: Detecting crack densities in images of fiber-reinforced polymerscitations
- 2019Optimization of the specimen geometry of unidirectional reinforced composites with a fibre orientation of 90° for tensile, quasi-static and fatigue tests
- 2016Hierarchical Architectures to Enhance Structural and Functional Properties of Brittle Materialscitations
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article
CrackDect: Detecting crack densities in images of fiber-reinforced polymers
Abstract
CrackDect is a tool to detect cracks in a given direction from a series of images. It is specialized to detect multiple matrix cracks in composite laminates to yield the crack density but can also be used as a general line detection. The package is written in Python, and includes classes and functions to efficiently handle large image stacks, pre-process images and perform the crack detection. Due to its modular structure it is easily expandable to other crack detection or feature recognition algorithms. Pre-processing of whole image stacks can be customized to account for different image capturing techniques. Since image processing tends to be computational and memory expensive, special focus is put on efficiency.