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Inline Monitoring of 3D Concrete Printing using Computer Vision

Abstract : The detection of anomalies is at the basis of any 3D printing control. In this paper, we propose a methodology for detection of anomalies based on computer vision. This methodology is composed of three modules: 1) image acquisition, 2) interlayer line and layer segmentation and 3) characterization of the local geometry and texture of the layers and detection of anomalies. The image acquisition is performed with a camera fixed to the printing nozzle. The proposed layer segmentation method recognizes and locates the lines separating the printed layers (F-score = 91%). The third module-taking as input the segmentation and the original image-evaluates the geometry of the layers and the texture of the material. The results are used to detect geometry anomalies when the values are outside the expected range. The material texture is classified into four classes of quality (macro-averaged F-score = 94%). We present the results and show the suitability of our methodology for automatic detection and localization of anomalies on images acquired during a printing session.
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Preprints, Working Papers, ...
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https://hal-mines-paristech.archives-ouvertes.fr/hal-03788879
Contributor : Petr Dokladal Connect in order to contact the contributor
Submitted on : Tuesday, September 27, 2022 - 10:10:43 AM
Last modification on : Saturday, October 22, 2022 - 5:13:12 AM

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  • HAL Id : hal-03788879, version 1

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Rodrigo Rill-García, Eva Dokladalova, Petr Dokládal, Jean-François Caron, Romain Mesnil, et al.. Inline Monitoring of 3D Concrete Printing using Computer Vision. 2022. ⟨hal-03788879⟩

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