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

Publikation: Beitrag in FachzeitschriftArtikelForschungBegutachtung

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.
OriginalspracheEnglisch
Aufsatznummer100832
Seitenumfang6
FachzeitschriftSoftwareX
Jahrgang16.2021
AusgabenummerDecember
DOIs
PublikationsstatusVeröffentlicht - 13 Okt. 2021

Bibliographische Notiz

Funding Information:
Part of this work has been performed within the COMET-project Experimental and numerical analysis of the damage tolerance behavior of manufactured induced defects and bonded repairs in structural aerospace composite parts (project-no.: VI-3.04 ) at the Polymer Competence Center Leoben GmbH (PCCL, Austria) within the framework of the COMET-program of the Federal Ministry for Transport, Innovation and Technology and the Federal Ministry for Digital and Economic Affairs with contributions by Montanuniversität Leoben (Chair of Designing Plastics and Composite Materials) and MAGNA Powertrain Engeneering Center Steyr GmbH CO KG. The PCCL is funded by the Austrian Government and the State Governments of Styria, Lower Austria and Upper Austria .

Publisher Copyright:
© 2021 The Authors

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