Deep learning-based image defect detection and removal in manufacturing

N. Gapon; V. Voronin; M. Zhdanova; R. Sizyakin; E. Semenishchev; Yu Ilyukhin

doi:10.1117/12.2682045

7 August 2023 Deep learning-based image defect detection and removal in manufacturing

N. Gapon, V. Voronin, M. Zhdanova, R. Sizyakin, E. Semenishchev, Yu Ilyukhin

Proceedings Volume 12624, Digital Optical Technologies 2023; 126241Q (2023) https://doi.org/10.1117/12.2682045
Event: SPIE Digital Optical Technologies, 2023, Munich, Germany

Conference Poster

Abstract

Image inpaintnig in textile manufacturing is a new emerging research topic in preprocessing for jacquard CAD systems. One of the most important aspects of a jacquard CAD system is the simulation of the appearance of a jacquard texture during inference. Jacquard image inpainting has become an indispensable process for the Jacquard CAD system. Jacquard image reconstruction is designed to restore a damaged image with missing information, so it is necessary to determine which parts of the image need to be repaired. Thus, this task includes two processing stages: the detection of defects and their recovery. This article presents a two-stage approach that combines new and traditional algorithms for detecting defects and repairing damaged areas. The first stage is a defect detection method based on a convolutional autoencoder (U-Net). The second stage is image inpainting based on exemplar-based concepts and the anisotropic gradient. Our system quantitatively outperforms state-of-the-art methods regarding reconstruction accuracy in the benchmark.

Citation Download Citation

N. Gapon, V. Voronin, M. Zhdanova, R. Sizyakin, E. Semenishchev, and Yu Ilyukhin "Deep learning-based image defect detection and removal in manufacturing", Proc. SPIE 12624, Digital Optical Technologies 2023, 126241Q (7 August 2023); https://doi.org/10.1117/12.2682045

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