Machine Vision in AI

Introduction

The technology in which a computer digitises an image, processes the data, and performs some kind of action is referred to as machine vision. It can be defined as a computer's ability to see and perceive the environment. It has various applications including object recognition, pattern recognition, etc.

It's critical to understand that the term machine vision can apply to a wide range of technologies, software and hardware products, integrated systems, operations, procedures, and skills.

Machine vision is a technical tool that can be creatively applied to existing technologies in order to solve problems in the real world. In addition to being utilised more frequently in other fields like security, autonomous driving, food production, packaging, logistics, and even in robots and drones, machine vision is growing in popularity within contexts for industrial automation.

What is Machine Vision in AI?

New innovative technologies are rapidly incorporating artificial intelligence (AI), and the capabilities of machine vision are being dramatically increased by the emerging field of deep learning models for AI.

A number of commercial Machine Vision systems have recently included artificial intelligence algorithms. These are a few illustrations of some of the real-world uses for artificial intelligence in machine vision.

• The Pulnix ZiCAM

It is a smart camera that does not require programming instead, it just receives good and bad parts and uses a hardware neural network to learn how to distinguish between them. The ZiCAM's recognition engine collects profiles, and pixel samples. The neural network, which has 74 outputs, receives these features and therefore be trained to divide products into up to 74 classes rather than just producing a pass-or fail report.

• The Sightech Eyebot

The Shape Eyebot and the Spectrum Eyebot are the two formats of the Eyebot. The Shape Eyebot can identify the shape of things that are placed in front of the camera and can then identify those that depart from the taught shape. The Spectrum Eyebot picks up the color of things that are displayed on it. An integer from 0 to 99 that represents the product's state can be the output of the system.

How does Machine Vision Work?

Systems for machine vision combine a variety of separate components into a single unit. The components comprise a communication system, an optical system, a vision processing system, sensors, and a lighting system.

Here, the optical system records nearby things including people, potential dangers, and other details. The visual information is then transmitted to a processor or onboard computer, which employs machine learning and artificial intelligence to interpret the images. The information is relayed back to the robot or other machines working alongside it once the photos and other data have been processed. From then, the machines can decide appropriately and interact with each other via a communication system.

Examples of Machine Vision

The following tasks can be carried out using machine vision technologies in a variety of industries.

• Self Driving Cars

To recognise objects on the road, self-driving cars use object recognition on images captured by cameras. Machine vision systems can locate items around them and identify potential hazards.

• Materials Inspection

Materials inspection systems with machine vision capabilities guarantee quality assurance. Machine vision inspects a variety of materials and goods for flaws, defects, and contamination. These devices, for instance, can check for flaws in the manufacturing process of pills and tablets.

• Diagnoses

With the use of technologies like magnetic resonance imaging, blood scans, and brain scans, pattern recognition is used in medical imaging analysis to make diagnoses.

Machine Vision Equipment

The following equipments are often required for machine vision systems

• Cameras

In a machine vision system, the cameras serve as the main piece of equipment for inspecting the object or item. A machine vision system can use a variety of cameras with various interfaces, pixels, resolutions, and functions.

• Smart Cameras

A smart camera has decision-making and description-generating capabilities and has all required communication connections and can connect to wifi or a server for quick image data transfer.

• Software

For operators to analyze and maintain a machine vision system, as well as program the hardware's functionality, the software is needed to visualize the data and display what the cameras are seeing.

• Embedded Systems

Also known as an imaging computer, embedded systems are directly connected to a processing board. This combines all parts under one single board computer.

• Frame Grabbers and Illuminators

Frame grabbers are a device that captures individual digital still frames from an analog video signal or a digital video stream and captures specific frames to analyze from a quick-moving system. Whereas, illuminators are added to cameras to provide enough light to capture the image depending on the details required.

• Lenses

What resolution the camera and machine vision system can capture images at will depend on the lenses. The resolution of a camera increases with the number of pixels.

• Computation

When using a capability like  machine learning, deep learning, or neural networks, the system normally needs high-performing computation to process the information more quickly.

• Cabling

To operate, a machine vision system needs power cables that link to the primary power source and user interfaces like laptops or data centres for real-time processing.

• Label Verification

Depending on the degree of adjustments required, a label verification unit can issue warnings, halt a moving system, verify an item, reject things from the process, and qualify alerts that are raised.

• Robots

Robots are to integrate with machine vision to boost productivity and precision, as well as to perform more difficult jobs that can only be completed if the system tells the robot precisely where to position the object.

Types of Machine Vision

• One-Dimensional Vision

Instead of assessing the image of a full object at once, one-dimensional vision analyses it one line at a time, frequently utilising a line-scan camera.

• Two-Dimensional Vision

Digital cameras are used to capture images for two-dimensional vision, which is then processed by analysing contrast differences across images.

• Three-Dimensional Vision

Three-dimensional vision creates a digital model of an object using many digital cameras and other sensors placed in various places, which allows for a precise evaluation of its position, size, and features.

Difference Between Machine Vision and Computer Vision?

What is a Machine Vision System?

Machine vision systems are assemblies of integrated electronic components, computer hardware, and software algorithms that offer operational guidance by processing and analyzing the images captured from their environment, and provide automatic image-capturing, evaluation, and processing capabilities. The data acquired from the vision system are used to control and automate a process or inspect a product or material.

What is Image Processing?

The process of converting an image into a digital format and carrying out specific procedures to extract some usable information from it is known as image processing. When implementing specific specified signal processing techniques, the image processing system typically interprets all images as 2D signals. main categories of image processing are visualization, recognition, restoration, pattern recognition, and retrieval.

Applications of Machine Vision

• Robot Guidance with Machine Vision

Robotic operations under the control of a machine vision can be performed using robotic guidance. Robots can readily be deployed in settings that are unsafe for manual operators, managing repeatable tasks with great precision and accuracy while operating continuously to achieve optimal efficiency.

• Object Detection

To make object detection as effective as feasible in this application of machine vision, several strategies are applied. In various areas of the manufacturing sector, including an assembly line, sorting, quality control, inventory management, etc., object detection is applied.

• Detection of Surface Defects

Another machine vision application that is surface fault identification. The precision and effectiveness of surface inspection can be achieved by machine vision in a simple-to-train model. Surface defect inspection is used in production to find flaws in casting parts, bearings, and various metal surfaces.