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How to avoid machine vision’s blind spots

11 April, 2013

Martin Gadsby, director of Optimal Industrial Automation, looks at some of the challenges in “difficult” machine vision applications, and outlines ways to tackle them.

Machine vision has become an essential element of quality and process control in many industries. Recent advances in camera technologies, processing power and software algorithms, are allowing users to automate many tasks that would not have been possible a decade ago.

But getting machine vision applications to work reliably and cost-effectively requires considerable skill and experience on the part of the system integrator. Frequently, decisions about the lighting, product presentation, camera fixturing and the operation of the machine vision system, can have as much of an impact on its performance as the choice of hardware and analysis technologies.

Product variations

Variable products make machine vision harder. Significant variations in the size or shape of the products being inspected by the system can create problems for a single fixed camera position, for example. Similarly, variations in product colour or surface finish can create challenges for the selection of appropriate lighting. In response to these issues, automatic or manually adjustable camera fixtures can be used to ensure that all the relevant parts of all products are in the image, and in focus, while lighting systems are available which adapt automatically to maintain image quality.

Smarter lighting can help in other ways too. Features that may be invisible under normal room lighting can be highly visible under controlled directional light or backlighting, for example. Beyond this, the use of UV, IR or even thermal cameras can turn apparently impossible features into clearly inspectable images.

Sometimes there are wide natural variations in the appearance of good products. Flexible items, such as confectionery packets, or those using non-synchronised printed foils, can vary significantly from one product to the next, for example. Advanced software approaches, including sophisticated calibration, pattern unwrapping and adaptive tools can help to overcome these issues.




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