Abstract
Recent advances in machine learning and computer vision have led to widespread use of these technologies in the industrial sector. Quality control and production counting are the most important applications. This article describes a solution for counting products in an industrial production line. It consists of two main modules: i) hardware infrastructure and ii) software solution. In ii) there are modules for image capture and product recognition using the Yolov5 algorithm and modules for tracking and counting products. The results show that our solution achieves 99.91% 99.91% accuracy in product counting and classification. Furthermore, these results were compared to the current manual counting system used in the industry considered in this study. This demonstrated the feasibility of our solution in a real production environment.
Keywords
Detection, Classification, Tracking, Counting, Machine learning, Deep learning e Industry 4.0
Notes
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Acknowledgments
This paper was only possible thanks to the help of the Semalo Indústria e Comércio de Alimentos and its workers. We thank the support of the UFMS (Universidade Federal de Mato Grosso do Sul), PET (Programa de Educação Tutorial – FNDE), FUNDECT, Finep, and Ministério da Ciência, Tecnologia, Inovações e Comunicações, funded by FNDCT. We also thank the support of the INCT of the Future Internet for Smart Cities funded by CNPq, proc. 465446/2014-0, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001, and FAPESP, proc. 2014/50937-1 and 2015/24485-9.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of FUNDECT, Finep, FAPESP, CAPES and CNPq.
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Fonte: https://link.springer.com/chapter/10.1007/978-3-031-10522-7_42
