The increased sophistication of artificial neural networks (ANNs) coupled with the availability of AI-powered chips have driven am unparalleled enterprise interest in computer vision (CV). This exciting new technology will find myriad applications in several industries, and according to GlobalData forecasts, it would reach a market size of $28bn by 2030. The increasing adoption of AI-powered computer vision solutions, consumer drones; and the rising Industry 4.0 adoption will drive this phenomenal change. Deep learning has bought a new change in the role of machine vision used for smart manufacturing and industrial automation. The integration of deep learning propels machine vision systems to adapt itself to manufacturing variations.
The "Industrial Machine Vision Market by Component (Hardware (Camera, Frame Grabber, Optics, Processor), and Software (Deep Learning, and Application Specific)), Product (PC-based, and Smart Camera-based), Application, End-User - Global Forecast to 2023" report has been added to ResearchAndMarkets.com's offering. The overall industrial machine vision market was valued at USD 7.91 Billion in 2017 and is expected to reach USD 12.29 Billion by 2023, at a CAGR of 7.61% between 2017 and 2023. This is because of the increasing need for quality inspection and automation, growing demand for AI and IoT integrated machine vision system, increasing adoption of Industrial 4.0, development of new connected technologies, and government initiatives to support smart factories, among others. Governments of different countries worldwide are encouraging investments in manufacturing, which is necessitating the use of various automation products for structural development. Software component is expected to grow at the highest rate between 2017 and 2023.
As manufacturing businesses are increasingly on the lookout for opportunities to boost automation, it is clear that big data and machine learning are set to revolutionize the future of production. Companies are already collecting huge amounts of data from various resources, including industrial machinery, and monitoring precise details of the production process in the hope of improving quality at every stage. When companies have gathered enough of the right data, predictive technologies can allow businesses to manage the servicing of machinery based on sensor data and advanced analytics, rather than on a fixed schedule. For a piece of machinery or an autonomous vehicle, for example, these technologies can help predict when and how they are likely to break down. Businesses can then service the equipment before it starts to be a problem and generate losses.
Artificial Intelligence is nothing new to anyone reading this blog, or most of the people on the planet. Siri, Alexa, and web chatbots have made AI commonplace. Yet, imagine what AI can do when you give it a pair of eyes and a training to analyze its surroundings. This is just what the combination of computer vision and machine learning offers to users. Machine learning is the application of statistical models and algorithms to perform tasks without the need to introduce explicit instructions.
AI has been part of our imaginations and simmering in research labs since a handful of computer scientists rallied around the term at a Research Workshop at Dartmouth College in 1956 and birthed the field of AI. Back in 1956, the dream of AI pioneers such as John McCarthy was to construct complex machines that possessed characteristics of human intelligence. However, general AI machines that replicate human senses, human reasoning, and think as we do are still mostly constrained to Hollywood and science fiction novels. AI today is, however, able to perform specific, comparably narrow tasks as well as, or sometimes better than, we humans can. Examples of narrow AI include applications such as classification of pathology from X-ray imagery, identification of people in Facebook photos via facial recognition, or your spam filters in Gmail.