Automating visual inspection with Machine Learning (ML)
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Learn how to automate high-volume quality inspection of products using computer vision and machine learning. Train a model to classify agricultural products based on their quality. Create a report on lemon quality.
At a Glance
Computer Vision paired with Machine Learning (ML) is becoming a popular way to automate the high-volume quality inspection of products in many industries. In this project, you will learn how to inspect quality of lemons by using basic ML methods of image classification.
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand. Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving, and whether there is something wrong in an image. Computer vision trains machines to perform these functions, but it has to do it in much less time with cameras, data, and algorithms rather than retinas, optic nerves, and the visual cortex. Because a system trained to inspect products or watch a production asset can analyze thousands of products a minute, noticing imperceptible defects or issues, it can quickly surpass human capabilities.
In this project, we will apply computer vision for the purpose of visual inspection. We will learn how to train a Machine Learning model to classify agricultural products based on their quality. Instead of using real cameras to capture images, we will use existing photographs to train our model. We will learn to form a DataSet from photographs of lemons and compare different types of classifiers. In the end, we will create a report that forms a DataSet on the lemons’ quality.
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