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Human head profile transitioning to brain with connected nodes and edge learning icon

Edge Learning

How Edge Learning Works

Cognex vision system suspended above conveyor belt inspects casted metal parts

Using a single, smart camera-based solution, edge learning can be deployed on any factory line within minutes. Such a solution integrates several components including machine vision hardware, rule-based tools, and artificial intelligence (AI) capabilities.

The In-Sight 2800 vision system, embedded with edge learning technology, facing left


Edge learning packs sophisticated hardware into a small form factor. It runs entirely in a smart camera equipped with integrated lighting, an autofocus lens, and a powerful sensor. All of these hardware features make edge learning possible.

Lighting is key for a high-quality imaging as it maximizes contrast, minimizes dark areas, and brings out the necessary detail.

A high-speed autofocus lens ensures that the object of interest is always in focus, even as distance changes. It does so by instantly adjusting focus as the region of interest (ROI) changes. Liquid autofocus lenses are smaller and lighter than equivalent mechanical lenses, reducing the size and weight of the camera while making it resistant to the shock and vibration of a production line.

A large and capable sensor offers high resolution and a wide field of view (FOV) for any given application.

Machine Vision Tools

Rule-based vision tools are well-suited for specialized tasks, such as location, measurement, and orientation. For the purposes of edge learning, they are combined in ways specific to the demands of factory automation, eliminating the need to chain vision tools or build complex logic sequences when training the system.

These tools provide fast preprocessing of images, extracting density, edge, and other feature information for the purposes of detecting and analyzing manufacturing defects. By identifying and clarifying the relevant parts of the image, these tools reduce computational load, compared to traditional deep learning approaches.

 Icons representing rule-based tools such as measure distance, pixel count and math and logic tools
Abstract image of human head with nodes and gears representing artificial intelligence

AI Capabilities

Instead of using rules created by human programmers, AI learns by example, building a neural network and devising effective pass/fail thresholds from labeled examples of acceptable and unacceptable parts. In essence, it mimics the way humans learn.

AI capabilities can have large training requirements. Edge learning, on the other hand, takes advantage of the fact that factory automation images have specific structural contents, and so pre-trains its algorithms with that domain knowledge. Not starting from scratch results in a less programming-intensive application.

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