Authors:
Henry Velesaca
1
;
2
;
Doménica Carrasco
1
;
Dario Carpio
1
;
Juan A. Holgado-Terriza
2
;
Jose Gutierrez-Guerrero
3
;
Tonny Toscano
1
and
Angel Sappa
1
;
4
Affiliations:
1
ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador
;
2
Software Engineering Department, University of Granada, 18014, Granada, Spain
;
3
Abbott Laboratories, 18004, Granada, Spain
;
4
Computer Vision Center, 08193-Bellaterra, Barcelona, Spain
Keyword(s):
Anomaly Detection, Industry 4.0, Deep Learning, OPC-UA, YOLO v8.
Abstract:
In the realm of industrial manufacturing, detecting defects in products is critical for maintaining quality. Traditional methods relying on human inspection are often error-prone and time-consuming. However, advancements in automation and computer vision have led to smarter industrial control systems. This paper explores a novel approach to identifying defects in industrial processes by integrating OPC-UA and YOLO v8. OPC-UA provides a secure communication standard, enabling seamless data exchange between devices, while YOLO v8 provides accurate object detection. By combining these technologies, manufacturers can monitor production lines in near real-time, analyze defects promptly, and take corrective actions. As a result, product quality and operational efficiency are improved. A case study involving tinplate lid defect detection demonstrates the effectiveness of the proposed approach. The system architecture, including PLC integration, image acquisition, and YOLO v8 implementation,
is detailed, followed by the performance evaluation of the OPC-UA server and YOLO v8 model integration. Results indicate efficient communication with low Round Trip Times and End-to-End delay, highlighting the potential of this approach for defect detection. The code is available at GitHub: https://github.com/hvelesaca/OPC-UA-YOLOv8-Lid-Anomaly-Detection, facilitating further research.
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