Design and Implementation of an IoT-Enabled Deep Learning Vision System for Automated Dimensional Measurement in Smart Manufacturing - Waluyo Nugroho; Afianto; Agus Ponco (Jurnal E-Komtek, 2025)
The rapid advancement of Industry 4.0 has brought the convergence of Internet of Things (IoT), computer vision, and deep learning to enhance automation and precision in manufacturing. This paper presents the design and implementation of an IoT-enabled deep learning vision system for automated dimensional measurement, integrated with programmable logic controller (PLC) control and real-time monitoring. The system employs a Raspberry Pi 5 as an edge computing unit, Logitech C270 camera for visual data acquisition, and an Omron CP2E PLC for process control. A YOLOv5 deep learning model is trained to detect and measure object dimensions with sub-millimeter accuracy. The Node-RED platform is utilized for dashboard visualization and communication, interfaced through Omron FINS protocol, with MySQL as the database for data logging. Experimental results show a high detection accuracy of 98.6% and an average measurement error of less than 0.5 mm, demonstrating the system’s effectiveness for smart manufacturing applications.