Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/336843 
Year of Publication: 
2025
Citation: 
[Journal:] Journal of Intelligent Manufacturing [ISSN:] 1572-8145 [Volume:] 37 [Issue:] 1 [Publisher:] Springer US [Place:] New York, NY [Year:] 2025 [Pages:] 399-415
Publisher: 
Springer US, New York, NY
Abstract: 
Improving the accuracy of sheet metal localization in industrial machines is of great interest to many automated manufacturing systems. Current vision-based systems typically rely on traditional image processing algorithms to locate the position of sheets in images. However, these algorithms often do not generalize robustly in real production setups. To achieve this, we propose a novel framework consisting of two deep learning models that locate sheets based on their corners, and a data generation pipeline capable of creating the annotated data required to train the models. Evaluation of this framework on real production data shows that the proposed approach locates sheet metal corners highly accurate with an average error of 2.17 pixels, which is at the edge of the theoretically achievable limit defined by the human annotation error in the test dataset. Extensive experiments show that the proposed framework generalizes well and can therefore be used as a backbone for various automated systems for which sheet metal localization is a relevant task.
Subjects: 
Deep learning
Computer vision
Key point detection
Synthetic data
Sheet metal processing
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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