ToolSeta Real-Synthetic Manufacturing Tools and Accessories Dataset

  1. Mauricio-Andres Zamora-Hernandez 1
  2. John Alejandro Castro-Vargas 2
  3. Jorge Azorin-Lopez 2
  4. Jose Garcia-Rodriguez 2
  1. 1 Universidad de Costa Rica
    info

    Universidad de Costa Rica

    San José, Costa Rica

    ROR https://ror.org/02yzgww51

  2. 2 Universitat d'Alacant
    info

    Universitat d'Alacant

    Alicante, España

    ROR https://ror.org/05t8bcz72

Liburua:
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020
  1. Álvaro Herrero (coord.)
  2. Carlos Cambra (coord.)
  3. Daniel Urda (coord.)
  4. Javier Sedano (coord.)
  5. Héctor Quintián (coord.)
  6. Emilio Corchado (coord.)

Argitaletxea: Springer Suiza

ISBN: 978-3-030-57801-5,  978-3-030-57802-2

Argitalpen urtea: 2021

Orrialdeak: 800-809

Biltzarra: International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO (15. 2020. Burgos)

Mota: Biltzar ekarpena

DIALNET GOOGLE SCHOLAR

Laburpena

The use of intelligent systems to improve manufacturing processes is the basis for the development of robotic solutions in Industry 4.0. Monitoring operators manipulating tools and objects is one of the key tasks. Deep learning methods are obtaining state-of-the-art results to solve this problem but large amounts of labelled data should be provided to these networks. However, no specific manufacturing tools datasets exist. For this purpose, we proposed a new dataset for this type of environment. An hybrid dataset of 29550 images has been proposed for network training that combines real and synthetic images of tools and components commonly used in manufacturing cells. This project is part of a set of proposed modules of a solution that allows us to evaluate in realtime the execution of assembly instructions of the operators throughout the production process.

Datuen iturria: Dialnet