Mr. Antoine Cordier Profile

Antoine Cordier

at Scortex

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Proceedings Article | 16 July 2021 Paper

Synthetic training data generation for deep learning based quality inspection

Pierre Gutierrez, Maria Luschkova, Antoine Cordier, Mustafa Shukor, Mona Schappert, Tim Dahmen

Proceedings Volume 11794, 1179403 (2021) https://doi.org/10.1117/12.2586824

KEYWORDS: Inspection, Manufacturing, Defect inspection, Defect detection

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Deep learning is now the gold standard in computer vision-based quality inspection systems. In order to detect defects, supervised learning is often utilized, but necessitates a large amount of annotated images, which can be costly: collecting, cleaning, and annotating the data is tedious and limits the speed at which a system can be deployed as everything the system must detect needs to be observed first. This can impede the inspection of rare defects, since very few samples can be collected by the manufacturer. In this work, we focus on simulations to solve this issue. We first present a generic simulation pipeline to render images of defective or healthy (non defective) parts. As metallic parts can be highly textured with small defects like holes, we design a texture scanning and generation method. We assess the quality of the generated images by training deep learning networks and by testing them on real data from a manufacturer. We demonstrate that we can achieve encouraging results on real defect detection using purely simulated data. Additionally, we are able to improve global performances by concatenating simulated and real data, showing that simulations can complement real images to boost performances. Lastly, using domain adaptation techniques helps improving slightly our final results.

Proceedings Article | 16 July 2021 Paper

Active learning using weakly supervised signals for quality inspection

Antoine Cordier, Deepan Das, Pierre Gutierrez

Proceedings Volume 11794, 1179413 (2021) https://doi.org/10.1117/12.2586595

KEYWORDS: Inspection, Machine vision, Visualization, Optical inspection, Manufacturing, Image processing

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Because manufacturing processes evolve fast and production visual aspect can vary significantly on a daily basis, the ability to rapidly update machine vision based inspection systems is paramount. Unfortunately, supervised learning of convolutional neural networks requires a significant amount of annotated images in order to learn effectively from new data. Acknowledging the abundance of continuously generated images coming from the production line and the cost of their annotation, we demonstrate it is possible to prioritize and accelerate the annotation process. In this work, we develop a methodology for learning actively,¹ from rapidly mined, weakly (i.e. partially) annotated data, enabling a fast, direct feedback from the operators on the production line and tackling a big machine vision weakness: false positives. These may arise with covariate shift, which happens inevitably due to changing conditions of the data acquisition setup. In that regard, we show domain-adversarial training² to be an efficient way to address this issue.

Proceedings Article | 20 June 2021 Presentation + Paper

Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection

Pierre Gutierrez, Antoine Cordier, Thaïs Caldeira, Théophile Sautory

Proceedings Volume 11787, 1178703 (2021) https://doi.org/10.1117/12.2591876

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The use of deep features coming from pre-trained neural networks for unsupervised anomaly detection purposes has recently gathered momentum in the computer vision field. In particular, industrial inspection applications can take advantage of such features, as demonstrated by the multiple successes of related methods on the MVTec Anomaly Detection (MVTec AD) dataset. These methods make use of neural networks pre-trained on auxiliary classification tasks such as ImageNet. However, to our knowledge, no comparative study of robustness to the low data regimes between these approaches has been conducted yet. For quality inspection applications, the handling of limited sample sizes may be crucial as large quantities of images are not available for small series. In this work, we aim to compare three approaches based on deep pre-trained features when varying the quantity of available data in MVTec AD: KNN, Mahalanobis, and PaDiM. We show that although these methods are mostly robust to small sample sizes, they still can benefit greatly from using data augmentation in the original image space, which allows to deal with very small production runs.

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