Automated fibre layup techniques are commonly used composite manufacturing processes in the aviation sector and require a manual visual inspection. Neural Network classification of defects has the potential to automate this visual inspection, however, the machine decision-making processes are hard to verify. Thus, we present an approach for visualising Convolutional Neural Network (CNN) based classifications of manufacturing defects and quantifying its robustness. Our investigations have shown that especially Smoothed Integrated Gradients and DeepSHAP are particularly well suited for the visualisation of CNN classifications. The Smoothed Integrated Gradients technique also reveals advantages in robustness when evaluating degraded input images.
|Title of host publication||Automated Visual Inspection and Machine Vision IV|
|Editors||Jurgen Beyerer, Michael Heizmann|
|Publication status||Published - 2021|
|Event||Automated Visual Inspection and Machine Vision IV 2021 - Virtual, Online, Germany|
Duration: 21 Jun 2020 → 25 Jun 2020
|Name||Proceedings of SPIE - The International Society for Optical Engineering|
|Conference||Automated Visual Inspection and Machine Vision IV 2021|
|Period||21/06/20 → 25/06/20|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- Composite Manufacturing
- Computer Vision
- Defect classifications
- Inline Inspection
- Laser Line Scan Sensor