Machine Vision System
Do you find that you’ve just invested in a machine vision system for your machine or assembly line, only to find that it doesn’t quite meet your expectations?
We are here to share with you, 3 common reasons why your Machine vision project would fail
1) Not outlining the objective
In a lot of project implementations, we find that the objectives are either undefined or requirements aren’t specified at the beginning of the project. So before you make any investments in equipment, ensure to list out your requirements. Specifying details such as the number of variants that are going to be inspected, all possible defects that you want the system to capture the minimum defect size, desired speed of inspection and desired accuracy.
2) Component choices for image acquisition
Remember, the objective is not for the image to look good to you but for the processing software, that’s actually looking at these images. Also, make sure that the images are captured with sufficient contrast so the features that are to be identified are clearly demarcated. So choices like Monochrome or color sensor, frame rate, resolution are to be selected with careful thought. Similarly, the choice of optics, illumination, and triggering are very important as well.
3) Applying the right image processing techniques
There are various choices of algorithms and tools that are provided with your software. If you’re building a system for the first time, we would recommend you invest in a commercial software license rather than trying to do this with open-source software. Of late AI and Deep Learning has also made massive strides in Machine vision, which makes teaching the software system a lot easier than what it was before. Make sure to check that out as well.Do you have any inputs or would like to know more? Please do let us know, we’d love to hear from you!
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