Galassi, Kevin ;
Caporali, Alessio ;
Palli, Gianluca
(2024)
INTELLIMAN. WP5 T5-1-1. Monocular Estimation of Connector Orientation: Combining Deformable Linear Object Priors and Smooth Angle Classification.
University of Bologna.
DOI
10.6092/unibo/amsacta/8079.
[Dataset]
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Abstract
The dataset contains data related to the development of a learning-based solution for the detection of manufacturing errors in the context of robotic wiring harness assembly. In particular, the dataset contains the training set, the validation set, and the test set used to develop the proposed neural networks-based error detector, and the scripts to train and test the neural networks and store the trained models. The data were produced in the framework of Horizon Europe INTELLIMAN project and are presented in the publication:
A. Caporali, K. Galassi, G. Berselli and G. Palli, "Monocular Estimation of Connector Orientation: Combining Deformable Linear Object Priors and Smooth Angle Classification," 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Boston, MA, USA, 2024, pp. 799-804, doi: 10.1109/AIM55361.2024.10637081.
Abstract
The dataset contains data related to the development of a learning-based solution for the detection of manufacturing errors in the context of robotic wiring harness assembly. In particular, the dataset contains the training set, the validation set, and the test set used to develop the proposed neural networks-based error detector, and the scripts to train and test the neural networks and store the trained models. The data were produced in the framework of Horizon Europe INTELLIMAN project and are presented in the publication:
A. Caporali, K. Galassi, G. Berselli and G. Palli, "Monocular Estimation of Connector Orientation: Combining Deformable Linear Object Priors and Smooth Angle Classification," 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Boston, MA, USA, 2024, pp. 799-804, doi: 10.1109/AIM55361.2024.10637081.
Tipologia del documento
Dataset
Autori
Settori scientifico-disciplinari
DOI
Contributors
Data di deposito
18 Dic 2024 14:11
Ultima modifica
18 Dic 2024 14:11
Risorse collegate
Nome del Progetto
Programma di finanziamento
EC - HE
URI
Altri metadati
Tipologia del documento
Dataset
Autori
Settori scientifico-disciplinari
DOI
Contributors
Data di deposito
18 Dic 2024 14:11
Ultima modifica
18 Dic 2024 14:11
Risorse collegate
Nome del Progetto
Programma di finanziamento
EC - HE
URI
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Statistica sui download
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