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Learning deep domain-agnostic features from synthetic renders for industrial visual inspection

Abstract : Deep learning has resulted in a huge advancement in computer vision. However, deep models require an enormous amount of manually annotated data, which is a laborious and time-consuming task. Large amounts of images demand the availability of target objects for acquisition. This is a kind of luxury we usually do not have in the context of automatic inspection of complex mechanical assemblies, such as in the aircraft industry. We focus on using deep convolutional neural networks (CNN) for automatic industrial inspection of mechanical assemblies, where training images are limited and hard to collect. Computer-aided design model (CAD) is a standard way to describe mechanical assemblies; for each assembly part we have a three-dimensional CAD model with the real dimensions and geometrical properties. Therefore, rendering of CAD models to generate synthetic training data is an attractive approach that comes with perfect annotations. Our ultimate goal is to obtain a deep CNN model trained on synthetic renders and deployed to recognize the presence of target objects in never-before-seen real images collected by commercial RGB cameras. Different approaches are adopted to close the domain gap between synthetic and real images. First, the domain randomization technique is applied to generate synthetic data for training. Second, domain invariant features are utilized while training, allowing to use the trained model directly in the target domain. Finally, we propose a way to learn better representative features using augmented autoencoders, getting performance close to our baseline models trained with real images.
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Submitted on : Tuesday, July 5, 2022 - 4:55:47 PM
Last modification on : Friday, August 5, 2022 - 11:43:20 AM

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Abdelrahman Abubakr, Igor Jovančević, Nour Islam Mokhtari, Hamdi Ben Abdallah, Jean-José Orteu. Learning deep domain-agnostic features from synthetic renders for industrial visual inspection. Journal of Electronic Imaging, SPIE and IS&T, 2022, 31 (05), pp.051604. ⟨10.1117/1.JEI.31.5.051604⟩. ⟨hal-03712960⟩

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