Machine Vision Is No Longer Expensive to Deploy
Due to the increasing levels of automation across different verticals, machine vision has become a critical component in machinery and operation workflows. Traditionally, machine vision is implemented based on computational-intensive approaches, such as edge extraction, line labeling, and polyhedral modeling. While machine vision has been used in quality control and sorting and assembly lines, the implementation is generally expensive and requires heavy upfront investments and a lengthy commissioning time.
In recent years, the emergence of cost-efficient hardware and quality accessible open-source software frameworks and tools have since lowered the barrier to entry significantly. Cloud-based deep learning algorithms, especially Convolutional Neural Networks (CNNs), are self-taught algorithms capable of finding and extracting unique patterns based on large amounts of data. Cost-efficient hardware, namely chipsets, cameras, and sensors, enable Artificial Intelligence (AI) inference to be performed at the edge, reducing the need for constant con…
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