| Full text | |
| Author(s): |
Xavier, Rafael J.
;
Viegas, Charles F. O.
;
Costa, Bruno C.
;
Ishii, Renato P.
;
Gervasi, O
;
Murgante, B
;
Hendrix, EMT
;
Taniar, D
;
Apduhan, BO
Total Authors: 9
|
| Document type: | Journal article |
| Source: | COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2022, PT I; v. 13375, p. 15-pg., 2022-01-01. |
| 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% 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) | |
| FAPESP's process: | 15/24485-9 - Future internet for smart cities |
| Grantee: | Fabio Kon |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 14/50937-1 - INCT 2014: on the Internet of the Future |
| Grantee: | Fabio Kon |
| Support Opportunities: | Research Projects - Thematic Grants |