Knowledge of visual inspection: Differences between AI, machine learning, and image processing.
I will explain the differences between AI, machine learning, deep learning, and procedural (rule-based) image processing!
This time, I would like to organize the types of AI. A new introduction page for DeepSky has also been created, so please take a look. Now, the term "AI" that is currently in the spotlight refers to "deep learning," which is the type of AI that can distinguish between pedestrians and traffic signs, and even defeat human professionals in chess and Go. In the world of image processing, there are also machine learning-based image processing and what is called procedural image processing. So, what are the differences between them? I think it will be easier to understand the meanings of each by looking at the diagram below. AI (Artificial Intelligence), as the name suggests, means artificial intelligence and has been the most widely used concept since around 1950, meaning "something that replaces human intelligence." In the world of image processing, "procedural" (rule-based) image processing, which processes images taken by a camera according to a set procedure to determine OK/NG, also falls under this category of AI. Our EasyInspector uses this type of AI across a wide range, including color judgment and dimensional angle inspection.
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Furthermore, around the 1980s, the method known as "machine learning" began to attract attention. In machine learning, instead of processing data through a fixed procedure to produce results, it started to be used as a method where the program itself generates the procedures needed to produce correct results. This requires "feeding a large amount of training data." For example, a machine learning program is given a large amount of data on the "color composition," "aspect ratio," and "area" of stamps (these are referred to as "features") along with the corresponding monetary values. As a result, the machine learning program becomes capable of determining the value of the next photographed stamp. Currently, the technology used in autonomous driving and DeepSky is "deep learning." In deep learning, even the "features" mentioned earlier are determined by the deep learning program itself. When searching for a part of an image that has certain characteristics, it automatically decides which features to extract from the image and optimally adjusts countless parameters to fit the purpose.
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Deep learning has made it possible to handle complex processes that would likely be impossible to fully write with procedural programming. Cases are increasingly emerging where image inspection can be used in places that previously did not adopt it due to reasons such as "difficult to set up" and "unstable detection." Please feel free to request our evaluation service (free of charge). List of support services: https://skylogiq.co.jp/index.php/supportservice/
