How to Choose a PC for Machine Vision Applications?
Machine vision is the technology that uses cameras and image processing software to inspect, measure, classify products or detect defects. It is used on production lines in applications such as quality control, barcode reading, part positioning and automatic inspection.
In machine vision applications, it is important that the computer can process images at sufficient speed. However, the required hardware varies with the number of cameras, image resolution, frames per second and the algorithms used.
When choosing a PC, first determine the camera interface and the image processing software. For example, suitable Ethernet connections should be checked for cameras using GigE Vision, and the required USB data transfer capacity for cameras using USB3 Vision. As the number of cameras and data traffic grow, network bandwidth and the performance the ports can provide at the same time become more important.
The main criteria to consider are:
- CPU: The image processing software's processor requirements and processing-time targets should be taken into account.
- GPU: May be required in applications using deep learning or GPU acceleration. Not every machine vision application requires a GPU.
- RAM: High-resolution images and multiple cameras may require more memory.
- Storage: If images will be saved, data volume and write speed should be calculated.
- Connections: Camera interfaces, Ethernet ports, add-in card slots and the required bandwidth should be checked.
- Timing: The time between capturing the image and producing the result should suit the speed of the production line.
Lighting, lens, camera position and the image processing algorithm also affect system performance. A powerful PC alone cannot solve problems caused by a wrongly chosen camera or insufficient lighting.
Conclusion: When choosing a PC for machine vision, the number of cameras, resolution, data transfer speed, algorithms and acceptable processing time should be evaluated together.