How the concept of machine vision are used in Robotics to configure sensors of Robots?


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Simply put, machine vision technology gives industrial equipment the ability to “see” what it is doing and make rapid decisions based on what it sees. The most common uses of machine vision are visual inspection and defect detection, positioning and measuring parts, and identifying, sorting, and tracking products.

Machine vision is one of the founding technologies of industrial automation. It has helped improve product quality, speed production, and optimize manufacturing and logistics for decades. Now, this proven technology is merging with artificial intelligence and leading the transition to Industry 4.0.

Machine vision can transform industrial operations 

Machine Vision System Architecture

Machine vision is a primary component of industrial automation. Explore the diagram above to learn how machine vision system components work together to transform operations.

How It All Started: Classic Machine Vision Systems

Machines could “see” before AI and machine learning. In the early 1970s, computers began using specific algorithms to process images and recognize basic features. This classic machine vision technology can detect object edges for positioning a part, find color differences that indicate a defect, and discern blobs of connected pixels that indicate a hole.

Classic machine vision involves relatively simple operations that don’t require artificial intelligence. The text has to be simple and sharp, like a bar code. Shapes have to be predictable and fit an exact pattern. A classic machine vision system can’t read handwriting, decipher a wrinkled label, or tell an apple from an orange.

Nevertheless, classic machine vision has had a huge impact on manufacturing. Machines don’t get tired, so they can spot defects faster and more reliably than human eyes. Plus, machines aren’t bound by the limits of human vision. Specialized machine vision cameras can use thermal imaging to detect heat anomalies and X-rays to spot microscopic flaws and metal fatigue.

The Rise of Artificial Intelligence: Deep Learning Inference and Industrial Machine Vision

Increasingly powerful edge computing —embedded and IoT devices at the network edge and beyond—plus a growing universe of deep learning models for artificial intelligence (AI) are radically expanding what machine vision can do. This rapid growth in capabilities is leading to the transformation of smart factories and Industry 4.0.

AI augments classic computer vision algorithms with models called neural networks. When a computer receives an image, or a video stream of images, machine vision software compares that image data with a neural network model. This process, called deep learning inference, allows computers to recognize very subtle differences like minuscule pattern mismatches in fabric and microscopic flaws in circuit boards.

To improve accuracy and speed, data scientists create specific neural network models for specific applications. During this process, called supervised training, a computer reviews tens of thousands of samples and identifies meaningful patterns, including patterns a human might not detect.

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