What is Machine Vision?

In simple terminology, machine vision is the technology that helps the machine to see. The advanced technology provides visual analysis for both still and moving objects. This tech is of tremendous use in manufacturing industries that are moving towards intelligent automation. Of course, that means that the robotics industry is making the most of machine vision software.

The increasing demand of end-users for superior quality products has facilitated a key role for machine vision software in the quality control department across all kinds of industries. Cameras are mounted over production lines to visually inspect the occurrence of any substandard product being manufactured. This software also guides the robots (specially kept for manufacturing the products) without human intervention. Although for intricate product detailing, expert vision is required. The advent of superior artificial intelligence has pushed machine vision software into becoming a cognitive software across all industry applications. But how was this possible?


A Shallow Dive Into Deep Learning

I’m sure you must have come across the term ‘Deep Learning’ one time or the other. So let’s have a quick recap on what exactly is deep learning? – Tech giants like Google, Apple, Facebook, and even Toyota have invested in deep learning technology. But it is still hard to find the explanation of exactly what deep learning is, especially in a simple language that everybody can understand. Deep learning is a sub-branch of machine learning that is concerned with neural networks. Neural networks are inspired by our very brains. The network is an interconnected web of nodes (neurons). These networks receive a set of inputs, perform aggressively complex calculations and eventually make use of the outputs to solve a problem.

Now, machine vision software when enabled with deep learning algorithms can increase high rates of visual identification and can consequently be used for fault inspection, product-piece positioning as well as automated handling of products.


How is This Any Different from Conventional Machine Vision?

In conventional machine vision technology, developers need to manually define and verify individual features for the machine in the software. However, due to the cognitive nature of deep learning algorithms, the machines can automatically find and extract patterns in order to differentiate between meticulously detailed and larger components being produced.

Machine vision software usually makes use of supervised deep learning algorithms in order to train the machine. A sample of training dataset (with labels) is provided to the machine to learn, identify and distinguish between objects. The system analyses this data and creates training models corresponding to the objects identified. Once a fresh set of testing data (without labels) is inserted into the system, the deep learning algorithm is able to assign a class/ label to the unlabelled data. Due to this allocation of classes, the items can now be identified automatically. The machine vision software continues to learn and relearn on-the-go. Deep learning processes are able to learn new things independently, without manual classification.


Expert Talk

MVtec HALCON is a premium software developed for machine vision with an integrated development environment that is being used worldwide. Usually, programming work can be pretty arduous and time consuming for pure manufacturing companies. Highly skilled developers in deep learning are required to build and train the machine vision software. MVtec enables cost savings and improves time efficiency for this market. HALCON’s flexible architecture facilitates rapid development for any kind of machine vision application.

A major application for deep learning in machine vision technology can be found in OCR – Optical Character Recognition. OCR is used for precisely identifying letter and number combinations. With deep learning, the typical features of each character are precisely identified based on defined classes. Identifying defects is a time-consuming affair. Especially in the case of tiny scratches on electronic devices. Experts would have to manually feed thousands of images for the machine to catch these tiny scratch defects – this would simply take too long. But deep learning tech can independently learn certain characteristics of defects and define them into corresponding ‘problem classes’. This can be used to identify small paint defects that are not visible to the naked eye.

It is no wonder that the Global Market for Machine Vision software is estimated to reach a whopping value of $24.8 billion by 2023, as per BBC research.

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