A Deep Learning Approach for Automatic Counting of Bales and Product Boxes in Industrial Production Lines

Autor(es)

Xavier, Rafael J. ; Viegas, Charles F. O. ; Costa, Bruno C. ; Ishii, Renato P. ; Gervasi, O ; Murgante, B ; Hendrix, EMT ; Taniar, D ; Apduhan, BO

• Tipo de documento: Artigo Científico
• Fonte: COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2022, PT I; v. 13375, p. 15-pg., 2022-01-01.

Resumo

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% 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. (AU)

• Processo FAPESP: 15/24485-9 - Internet do futuro aplicada a cidades inteligentes
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 14/50937-1 - INCT 2014: da Internet do Futuro
• Beneficiário: Fabio Kon
• Modalidade de apoio: Auxílio à Pesquisa - Temático