ISSN 2594-5300
52º Seminário de Aciaria, Fundição e Metalurgia de Não-Ferrosos — vol. 52, num.52 (2023)
Título
DOI
Downloads
Resumo
For recycling-based steel production, an on-site scrap quality assessment is mandatory to control the received quality from the supplier. This process can be automated with computer vision technology. We developed an application based on deep learning techniques that assess the scrap by considering different quality characteristics and thus, providing decision support for the scrap yard staff. A graphical user interface allows to continuously review the automatically detected scrap in a simple and quick way. Guided by the scrap yard staff's expertise, the detection accuracy increases over time without specialist support needed from IT experts.
For recycling-based steel production, an on-site scrap quality assessment is mandatory to control the received quality from the supplier. This process can be automated with computer vision technology. We developed an application based on deep learning techniques that assess the scrap by considering different quality characteristics and thus, providing decision support for the scrap yard staff. A graphical user interface allows to continuously review the automatically detected scrap in a simple and quick way. Guided by the scrap yard staff's expertise, the detection accuracy increases over time without specialist support needed from IT experts.
Palavras-chave
Scrap management; machine learning; scrap classification; object detection
Scrap management; machine learning; scrap classification; object detection
Como citar
Kempken, Jens.
IMPROVEMENT OF SCRAP MANAGEMENT IN EAF AND BOR BY CLASSIFICATION USING COMPUTER VISION ALGORITHMS
,
p. 534-547.
In: 52º Seminário de Aciaria, Fundição e Metalurgia de Não-Ferrosos,
São Paulo,
2023.
ISSN: 2594-5300
, DOI 10.5151/2594-5300-40234
