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Performance Evaluation of Computer Vision Algorithms in a Programmable Logic Controller: An Industrial Case Study.

Rodrigo Vieira¹, Dino Silva¹, Eliseu Ribeiro^1,2

¹School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, Portugal.

Sensors (Basel, Switzerland)

|February 10, 2024

Summary

This summary is machine-generated.

This study assesses programmable logic controllers (PLCs) for image processing. While powerful for industrial control, PLCnext controllers show feasibility for simple machine vision tasks when integrated with Python and OpenCV.

Keywords:

OpenCV computer vision performance benchmark programmable logic controllers

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Area of Science:

Industrial Automation
Computer Vision
Embedded Systems

Background:

Programmable Logic Controllers (PLCs) traditionally focus on industrial control tasks.
Modern PLCs, like Phoenix Contact's PLCnext, incorporate Linux OS, enabling advanced computing capabilities.
Integrating computer vision within industrial automation offers potential for enhanced quality control and process optimization.

Purpose of the Study:

To evaluate the PLCnext controller as a viable platform for image processing tasks.
To assess the performance of PLC-based image processing against traditional computer systems.
To demonstrate a practical application in industrial quality control via visual inspection.

Main Methods:

Implementing visual processing applications using Python and the OpenCV library on the PLCnext controller.
Benchmarking the PLCnext controller's image processing performance against a standard computer.
Developing a demonstration application for visual inspection-based quality control.

Main Results:

The PLCnext controller, despite processing power limitations, can execute image processing tasks.
Performance benchmarks indicate feasibility for less complex industrial machine vision applications.
A functional demonstration for quality control highlights the potential of integrated systems.

Conclusions:

The simultaneous use of PLCnext controllers for both industrial control and image processing is feasible for specific applications.
Low-complexity tasks with undemanding cycle times are suitable for this integrated approach.
This research provides valuable benchmarks for the convergence of industrial automation and computer vision.