Machine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance usually in the industry, and they refer to the tech that has brought about some real change in the industry.  Machine vision refers to many technologies, software and hardware products, integrated systems, actions, methods, and expertise. And at Shivalik all of this expertise and the sound tech of tomorrow are taught to students with great enthusiasm so that they can be the tomorrow of the industry and become the link between the consumer and the technology world to produce maximum output from their own work with better efficiency. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real-world problems. The term is the prevalent one for these functions in industrial automation environments but is also used for these functions in other environments vehicle guidance. The overall machine vision process includes planning the details of the requirements and project and then creating a solution. During run-time, the process starts with imaging, followed by automated analysis of the image and extraction of the required information.

Machine vision” vary, but all include the technology and methods used to extract information from an image on an automated basis, as opposed to image processing, where the output is another image. The information extracted can be a simple good-part/bad-part signal, or more a complex set of data such as the identity, position, and orientation of each object in an image. The information can be used for such applications as automatic inspection and robot and process guidance in industry, security monitoring, and vehicle guidance. This field encompasses a large number of technologies, software and hardware products, integrated systems, actions, methods, and expertise. Machine vision is practically the only term used for these functions in industrial automation applications; the term is less universal for these functions in other environments such as security and vehicle guidance. it is a system of tomorrow and just like the other future technologies it is taught and introduced to students in the form of webinars, training programs, and other quality workshops to help them understand such complex tech better and get them hands-on experience in working in the same for a better understanding and chance of opting the same as a career option according to their choice.

Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of basic computer science; machine vision attempts to integrate existing technologies in new ways and apply them to solve real-world problems in a way that meets the requirements of industrial automation and similar application areas.  The term is also used in a broader sense by trade shows and trade groups such as the Automated Imaging Association and the European Machine Vision Association. This broader definition also encompasses products and applications most often associated with image processing. The primary uses for machine vision are automatic inspection and industrial robot/process guidance. Deep Learning” has variable meanings, most of which can be applied to techniques used in machine vision for over 20 years. However, the usage of the term in Machine Vision began in the later 2010s with the advent of the capability to successfully apply such techniques to entire images in the industrial machine vision space.  Conventional machine vision usually requires the “physics” phase of a machine vision automatic inspection solution to create reliable simple differentiation of defects.

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. Industrial machine vision is the backbone of smart manufacturing, logistics, and operations. Machine vision cameras, embedded IoT sensors, and industrial PCs can bring intelligence, analysis, and efficiency to every step of the manufacturing process. Machine vision applied to manufacturing can improve product quality and overall system efficiency, increasing the throughput of your manufacturing line, reducing labor costs, and freeing up your staff to focus on higher-value work.

Improvements to worker health and safety are a critical benefit of applying machine vision to operations. AI-powered computer vision can ensure workers are maintaining social distance and wearing proper safety equipment. Robots and equipment with machine vision can interpret human actions and interact, helping prevent accidents before they happen. If a situation is unsafe, they can warn the operator or shut equipment down automatically, reducing the risk for your employees and your company.

Written By:- SHASHANK MISHRA