Deep Learning and Edge Learning are all subsets of artificial intelligence. However, Edge Learning and Deep Learning both have their own distinct characteristics.
In all stages of implementation, Edge Learning is easier to use than Deep Learning. It requires fewer images to achieve proof of concept, less time to set up and collect images, and no specialized programming. However Edge Learning and Deep Learning have their own use cases.
Deep Learning simulates the interconnected neurons in the human brain, strengthening and weakening the connections to create visual understanding. In Deep Learning, hundreds of layers of neural networks are exposed to a large set of images of objects. By slightly modifying the connections within and between these layers each time a new image is exposed. Deep Learning learns to reliably identify such objects and detect errors in those objects without the need for intensive training.
Deep Learning provides the ability to process large and highly detailed images. Ideal for complex or highly customizable applications. With such applications comes significant changes, requiring computational power and intensive training capabilities. To account for this variation and capture all potential outcomes, a numbered set of hundreds or thousands of images must be used for training. Deep learning enables users to analyze images quickly and efficiently, providing an efficient solution for automating complex tasks. Most factory automation applications require low complexity and are therefore better suited for Edge Learning.
The power of AI can be applied in factory automation problems by adding knowledge of application requirements to neural network connections from the start. This training eliminates a lot of the hassle, especially when aided by the right machine vision tools.
Edge Learning can be trained in minutes, using only 5 - 10 images. Compare this to Deep Learning solutions, which can require hours to days of training, using hundreds to thousands of images. By streamlining implementations. Edge Learning technology allows manufacturers to evolve quickly, while remaining flexible and being able to easily adapt to changes.
To optimize Edge Learning working on embedded system, training images are scaled or fixed, analyzing only specific area of interest. If these thumbnails are analyzed by engineers, they can be confident that the edge learning tools work the same.
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