{"id":71151,"date":"2024-03-05T12:16:21","date_gmt":"2024-03-05T11:16:21","guid":{"rendered":"https:\/\/intellias.com\/?post_type=blog&p=71151"},"modified":"2024-07-01T17:49:07","modified_gmt":"2024-07-01T15:49:07","slug":"top-computer-vision-applications-across-5-industries","status":"publish","type":"blog","link":"https:\/\/intellias.com\/top-computer-vision-applications-for-industries\/","title":{"rendered":"Top Computer Vision Applications Across 5 Industries"},"content":{"rendered":"

With artificial intelligence (AI) on the rise, algorithms are getting better and better at visual tasks. Today\u2019s computer vision applications can already read texts with ease. They can identify objects, classify them, and track their movement. They can recognize human faces and convincingly transform them. Moreover, computer vision makes machines comprehend and interpret visual data: from medical imaging to fraud detection, to autonomous driving<\/a> \u2013 the technology is firmly on the way to revolutionize virtually every industry sector.<\/p>\n

Consequently, various businesses, whether digitally native or brick-and-mortar, are increasingly utilizing computer vision programs for their operations or exploring novel applications for this technology.<\/p>\n

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Computer vision is not just about building systems that see, but building systems that can interpret what they see.<\/p>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t <\/span> Steve Jobs<\/span><\/span>\n\t\t\t\t<\/small>\n\t\t\t<\/blockquote>\n\t\t<\/section>\n

Whether you\u2019re familiar with AI, machine learning<\/a> and computer vision or new to the concepts, read on. We\u2019ll define computer vision and explore its growth and how it works. Finally, we\u2019ll take you on a tour of computer vision applications being used and refined across five major industries. Almost every sector has use cases for computer vision, but we\u2019ll look at transportation, healthcare, manufacturing, retail, and agriculture. During this exploration, we’ll showcase everyday examples of computer vision applications, illustrating how these technologies are widespread in our daily lives, often without us explicitly recognizing their reliance on computer vision.<\/p>\n

Defining computer vision<\/h2>\n

First, how do they define computer vision? Let\u2019s start with the basics. SImplistically, computer vision technology is the field of computer science that enables computer systems to see and understand the world around them, make decisions about what they see, and act accordingly.<\/p>\n

Looking for a more technical definition? Computer vision (CV) 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 to take actions or make recommendations based on that information.
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What is computer vision vs. machine vision?<\/h3>\n

There\u2019s a subtle but important distinction between computer vision and machine vision. Computer vision relies on machine learning and uses enormous processing power. Computer vision systems collect as much visual data as possible and then process that information so it can be applied to various tasks. That\u2019s what gives computer vision applications their flexibility.<\/p>\n

Machine vision is a lighter-weight subset of computer vision. Machine vision typically focuses on a narrow task. In manufacturing, machine vision (or robot vision) is often used for quality control and to guide objects down an assembly line. We will discuss this further in the section about computer vision and manufacturing.<\/p>\n

The goal of computer vision<\/h2>\n

\"What<\/p>\n

Computer vision aims to replicate the complexity of human vision. How? By giving computers a way to interpret and understand the world through images. Computer vision applications rely on visual artificial intelligence. The machines are trained on massive datasets of visual information in a process called machine learning. This is the same process used to train other artificial intelligences. The only difference is that the data is in a visual format in computer vision applications.<\/p>\n

With enough training, AI software can make sense of visual inputs, but most computer vision technology doesn\u2019t approach human vision. AI still struggles with adaptability, handling ambiguity, and context-based understanding. For example, an early release of Stability\u2019s AI model recognized that a certain element was present on many photos in its training data. Its art generator, Stable Diffusion, started putting that element in photorealistic images. Unfortunately, the AI didn\u2019t have the context to understand what the element really was. It was the Getty Images logo<\/a>, and using it was an infringement on Getty\u2019s trademark. Stable Diffusion was also telling on itself for training with Getty\u2019s photos without permission.<\/p>\n

That said, computer vision technology is impressive and has many use cases. AI is better than humans at some visual tasks and is almost always faster. But before we dive into the use of computer vision in different industries, let\u2019s look at how computer vision technology works today.<\/p>\n

How we \u201csee\u201d the world through machine eyes today<\/h2>\n

Computer vision systems use a combination of hardware and software to extract, analyze, and understand visual information. This information can come from an image or a sequence of images (in other words, a video). In very simple terms, the steps of computer vision include:<\/p>\n

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  1. Machine learning<\/strong>: An algorithm is trained on massive visual datasets<\/li>\n
  2. Input<\/strong>: Cameras, sensors, and other imaging devices capture visual data<\/li>\n
  3. Processing<\/strong>: The CV algorithm analyzes the input and identifies patterns, objects, and relationships<\/li>\n
  4. Decision-making<\/strong>: The machine uses analytics to make informed decisions or predictions<\/a><\/li>\n
  5. Action<\/strong>: The machine performs a task based on its visual analysis and decision-making<\/li>\n<\/ol>\n

    Computer vision has been around for decades, but recent developments in artificial intelligence have transformed the processing and decision-making steps. With modern neural network technology, computer vision systems have shot from 50% accuracy to 99% accuracy<\/a> in less than ten years. That means that in some contexts, computer vision is now comparable to human vision for recognizing and responding to visual input.<\/p>\n

    \"Computer<\/p>\n

    Consider these computer vision methods and the complex tasks they make possible:<\/p>\n

    Recognizing and classifying objects<\/h3>\n

    Computer vision techniques can identify and categorize objects within images with impressive accuracy. This extends to faces, animals, vehicles, specific products, and even complex scenes.<\/p>\n

    Examples from everyday life include:<\/p>\n