REMODEL. WP4. Vision-based Perception. T4_3. Cable Detection and Tracking. Segmentation of Deformable Linear Objects. v0

Caporali, Alessio ; Zanella, Riccardo ; Palli, Gianluca (2022) REMODEL. WP4. Vision-based Perception. T4_3. Cable Detection and Tracking. Segmentation of Deformable Linear Objects. v0. University of Bologna. DOI 10.6092/unibo/amsacta/7030. [Dataset]
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Abstract

The dataset contains the source code and model weights utilized for the experimental validation on segmentation of deformable linear objects, associated to a novel algorithm called Ariadne+. The proposed approach uses deep learning and standard computer vision techniques aiming at their reliable and time effective instance segmentation of wires. The source code comprises a deep convolutional neural network employed for generating a binary mask showing where wires are present in the input image, and graph theory applied to create the wire paths from the binary mask through an iterative approach maximizing the graph coverage. In addition, a B-Spline model of each instance is provided. The dataset is associated to the related publication: A. Caporali, R. Zanella, D. D. Greogrio and G. Palli, "Ariadne+: Deep Learning--Based Augmented Framework for the Instance Segmentation of Wires," in IEEE Transactions on Industrial Informatics, vol. 18, no. 12, pp. 8607-8617, Dec. 2022, doi: 10.1109/TII.2022.3154477.

Abstract
Tipologia del documento
Dataset
Autori
AutoreAffiliazioneORCID
Caporali, AlessioUniversity of Bologna
Zanella, RiccardoUniversity of Bologna
Palli, GianlucaUniversity of Bologna
Settori scientifico-disciplinari
DOI
Contributors
Contributor
Affiliazione
Tipo
Caporali, Alessio
University of Bologna
Contact person
Data di deposito
27 Ott 2022 12:52
Ultima modifica
27 Ott 2022 12:52
Risorse collegate
Tipologia
Relazione
Identificativo
Nome del Progetto
REMODEL - Robotic tEchnologies for the Manipulation of cOmplex DeformablE Linear objects
Programma di finanziamento
EC - H2020
URI

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