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Vision industrielle et réseaux de neurones profonds: Application au dévracage de pièces plastiques industrielles

Julien Langlois 1, 2
Abstract : This work presents a pose estimation method from a RGB image of industrial parts placed in a bin. In a first time, neural networks are used to segment a certain number of parts in the scene. After applying an object mask to the original image, a second network is inferring the local depth of the part. Both the local pixel coordinates of the part and the local depth are used in two networks estimating the orientation of the object as a quaternion and its translation on the Z axis. Finally, a registration module working on the back-projected local depth and the 3D model of the part is refining the pose inferred from the previous networks. To deal with the lack of annotated real images in an industrial context, an data generation process is proposed. By using various light parameters, the dataset versatility allows to anticipate multiple challenging exploitation scenarios within an industrial environment.
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https://hal.archives-ouvertes.fr/tel-02925143
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Submitted on : Friday, August 28, 2020 - 5:34:43 PM
Last modification on : Tuesday, January 5, 2021 - 4:26:09 PM

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  • HAL Id : tel-02925143, version 1

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Julien Langlois. Vision industrielle et réseaux de neurones profonds: Application au dévracage de pièces plastiques industrielles. Traitement des images [eess.IV]. Université de Nantes, 2019. Français. ⟨tel-02925143⟩

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