How In-Sight 2800 makes vision automation simple

Jun 8, 2022 | Advertorials, Machine Vision, Machine Vision & Systems

The new In-Sight 2800 vision system puts the power of a full-featured vision system into an easy-to-use package that gets applications running in minutes. From simple presence/ absence detection to advanced categorization and sorting tasks, In-Sight 2800 provides an easy to deploy solution for error-proofing.

The system is intended for use by line and automation engineers to solve challenging factory automation problems, without requiring knowledge of deep learning or machine vision. An engineer can turn on the In-Sight 2800 and have it recognise and classify defects within minutes. And it can do so with an unlimited number of classes, thus solving even more advanced categorisation and sorting tasks.

High ease of use accommodates all skill levels and expedites deployment

In-Sight 2800 is designed to be straightforward to set up, with no advanced programming needed. Training the system to solve a problem is very much like training an attentive new employee on the line. The engineer shows examples with distinctions that need to be made, and embedded edge learning can quickly make the same distinctions.

Edge learning is a subset of deep learning in which processing takes place directly on-device using a set of pre-trained algorithms. The technology is simple to setup, requiring less time and fewer images for training, compared to more traditional deep learning-based solutions. Deploying the In-Sight 2800’s edge learning tools takes minutes, a handful of training images, and the attention of an engineer who understands the problem they need to solve, but who doesn’t necessarily have specific vision or deep learning knowledge.

Multi-class functionality addresses wide range of tasks

One key competency of edge learning is its ability to quickly and reliably separate parts into categories, after being trained on labeled images of those parts in the designated categories. A common application for this function is to classify acceptable and unacceptable parts as OK/NG.

Users train edge learning classification tools by providing images of both acceptable and unacceptable parts. There is no need to mark or define what makes a part unacceptable. Instead, the tool itself weights which variations in a part are significant for making a determination, while ignoring variations that do not affect the classification. The edge learning tools, embedded within the In-Sight 2800, can also handle classifications that are much more complex than a binary OK/NG decision. The ability to define multiple classifications provides the ability to solve a higher number of factory automation problems.

Multiple regions of interest (ROI) functionality focuses on essential features

To refine an inspection application, a line engineer can use their knowledge of what the significant variable areas are on the part to define specific focus areas, called region of interest (ROI). Through an intuitive interface, configuring applications on the In-Sight 2800 is simple using familiar click-and-drag tools. One drag defines a box, another moves it. The box can be locked to invariant features of the part.

This makes it straightforward to perform assembly verification for complex assemblies with many different configurations and variable parts, such as printed circuit boards (PCBs). Such problems previously took an immense amount of work to decide which features should be checked to confirm that the right part had been installed, and then program the vision system to examine those features. In-Sight’s edge learning tools make these determinations autonomously, meaning the engineer can focus on higher value-add activities, like optimising their operations.

Easy-to-use machine learning with unparalleled flexibility

Developed from years of experience in factory automation, Cognex vision tools are specifically focused on line operations requirements. The advantage of this well-honed technology becomes clearer as errors become subtler and harder to detect.

For example, a rotary capper can misthread, damage, or leave a gap when capping a bottle. Many systems can easily detect large, visible errors. The difference emerges when the gap is almost imperceptible. Other systems may pass such an error, causing potential leaks or contamination. In-Sight 2800, with its combination of edge learning and focused machine vision tools, will classify those almost invisible flaws as unacceptable.

Easy to use, trainable with a handful of images, capable of multi-class and multi-ROI operations, the In-Sight 2800 vision system is transforming factory automation.

Learn more:


Contact Cognex:

Phone: +44 121 296 5163