Proper lighting design is essential to assure a successful machine vision project. Ignoring this is one of the most common causes of machine vision project failures.
Machine vision lighting is a broad topic but a short article can be useful because some core concepts are not widely known. We’ll start with three core statements.
We review the “state of the market” and discuss some established technologies that are maturing to provide value to more end users, as well as some “cutting-edge” technologies that may bear watching.
Deep learning software represents a powerful tool in the machine vision toolbox, but one must first understand how the technology works and where it adds value.
In the machine vision marketplace the term “AI” typically refers to deep learning platforms that enable industrial automation and inspection. To appreciate the value proposition of AI in this context, it’s helpful to understand how the technology has evolved over the past several decades.
Deep learning is now more user-friendly and practical than ever and together with other vision technologies opens up new application areas, making the inclusion of vision inspection as part of Industry 4.0 even more beneficial.
In this article, deep learning refers to developments during the last few years that have enabled applying the technique to entire images in the industrial machine vision space.
While machine vision applications have been highly successful for decades using "analytical" vision tools, deep learning is able to successfully solve very complex classification and object detection problems with ease.
Product packaging - in an extremely broad range of markets from food to pharma - frequently incorporates an extremely important sealing technology called “tamper evident” seals.