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This work was partially support by the Programme for Attracting Talent of University of Cadiz, and was developed in the framework of the AUROVI Project, supported by the Ministry of Science, Innovation and Universities under grant EQC2018-005190-P

Analysis of institutional authors

Badesa, Francisco JCorresponding Author

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August 4, 2024
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A Neural-Network-Based Cost-Effective Method for Initial Weld Point Extraction from 2D Images

Publicated to:Machines. 12 (7): 447- - 2024-07-01 12(7), DOI: 10.3390/machines12070447

Authors: Lopez-Fuster, MA; Morgado-Estevez, A; Diaz-Cano, I; Badesa, FJ

Affiliations

Univ Cadiz, Appl Robot Grp, Cadiz 11003, Spain - Author
Univ Politecn Madrid, Ctr Automat & Robot CAR UPM CSIC, Madrid 28006, Spain - Author

Abstract

This paper presents a novel approach for extracting 3D weld point information using a two-stage deep learning pipeline based on readily available 2D RGB cameras. Our method utilizes YOLOv8s for object detection, specifically targeting vertices, followed by semantic segmentation for precise pixel localization. This pipeline addresses the challenges posed by low-contrast images and complex geometries, significantly reducing costs compared with traditional 3D-based solutions. We demonstrated the effectiveness of our approach through a comparison with a 3D-point-cloud-based method, showcasing the potential for improved speed and efficiency. This research advances the field of automated welding by providing a cost-effective and versatile solution for extracting key information from 2D images.

Keywords

Computer visionInitial weld pointIntelligent weldingObject detectionPoint cloudRobotic weldingRoboticsShipbuildingYolYolo

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Machines due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 74/182, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Engineering, Mechanical. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Industrial and Manufacturing Engineering.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-11-10:

  • Scopus: 2

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-11-10:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 8 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
    • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/83799/

    As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

    • Views: 134
    • Downloads: 37

    Leadership analysis of institutional authors

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (BADESA CLEMENTE, FRANCISCO JAVIER).

    the author responsible for correspondence tasks has been BADESA CLEMENTE, FRANCISCO JAVIER.