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Edge Learning

Using Artificial Intelligence in Machine Vision

Edge learning icon and brain with connected nodes against image of factory floor

Since the term “artificial intelligence” was coined on the campus of Dartmouth College in 1956, it has become common nomenclature across many fields of study – from its origins in philosophy to science, mathematics, and beyond. Despite having existed for decades, the integration of artificial intelligence (AI) into machine vision is relatively new. Today, more and more manufacturers are leveraging the combined power of AI and machine vision to better automate, optimize their efficiency, and improve quality control.

AI augments rule-based machine vision with image-based analysis. When a computer (or vision system) receives an image, AI software compares that image with a database consisting of both “good” and “bad” reference images and outputs a result. At a minimum, the result is pass/fail or OK/NG but can scale in complexity depending on requirements. This process of learning to recognize patterns and infer from annotated reference images allows computers to differentiate between acceptable and unacceptable anomalies in objects under inspection.

Moreover, machine vision solutions embedded with AI technology can use natural language processing to read and interpret labels on images, compared to rule-based approaches which require extensive programming and significant technical expertise. This enables a wider base of users to take advantage of AI for factory automation. Two leading technologies within AI – edge learning and deep learning – help to further simplify automation of highly variable tasks and solve tasks that are too complicated and time-consuming to program with rule-based algorithms.

Edge Learning – Edge learning is a subset of AI in which processing takes place on-device, or “at the edge” of where the data originates, using a pre-trained set of algorithms. The technology is simple to setup, requiring smaller image sets and shorter training and validation periods than traditional deep learning-based solutions.

Deep Learning – Capable of processing large, detailed image sets, deep learning is designed to automate complex or highly customized applications. The technology enables users to analyze vast image sets quickly and efficiently, to detect subtle defects and deliver accurate results.



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