TY - JOUR

T1 - CrackDect: Detecting crack densities in images of fiber-reinforced polymers

AU - Drvoderic, Matthias

AU - Rettl, Matthias

AU - Pletz, Martin

AU - Schuecker, Clara

N1 - Publisher Copyright: © 2021 The Authors

PY - 2021/10/13

Y1 - 2021/10/13

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85121998908&partnerID=8YFLogxK

U2 - 10.1016/j.softx.2021.100832

DO - 10.1016/j.softx.2021.100832

M3 - Article

VL - 16.2021

JO - SoftwareX

JF - SoftwareX

SN - 2352-7110

IS - December

M1 - 100832

ER -