Algorithm assessment for layup defect segmentation from laser line scan sensor based image data
2020
The Automated Fiber Placement process is established in the aerospace industry for the production of composite
components. This technique places several narrow material strips in parallel. Within current industrial Automated Fiber Placement processes the visual inspection takes typically up to 50 % of overall production time.
Furthermore, inspection quality highly depends on the inspector. Therefore, automation of visual inspection
offers a great improvement potential. To ensure reliable defect detection the segmentation of individual defects
must be investigated. For this reason, this paper focusses on an assessment of defect segmentation algorithms.
Therefore, 29 structural, statistical and spectral algorithms from related work were assessed, theoretically, using
the 12 most relevant criteria as assessed from literature and process requirements. Then, seven most auspicious
algorithms were analysed in detail. For reasons of determinism, Neural Network approaches are not part of
this paper. Manually labelled prepreg defect images from a laser line scan sensor were used for tests. The test
samples contain five defect types with 50 samples of each. Additionally, layups without defects were analysed.
It was concluded that Adaptive Thresholding works best for global defect segmentation. The Cell Wise Standard
Deviation Thresholding performs also quite well, but is very sensitive to grid size. Feasible algorithms perform
reliable defect segmentation for layed up material.
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