Machine vision (MV) is a solution that uses computers and algorithms to interpret, analyze, and understand surrounding visual information, and is a combination of different technologies and methods primarily used to automatically extract image information to provide operational guidance and key data for machines to perform a given task in both industrial and non-industrial environments.

330190-040-00-00 In the application, the machine vision system consists of multiple cameras that capture, interpret, and send signals to the control system. New research into the machine vision market shows that advances in cameras, artificial intelligence, and chipsets are driving the potential for the use of machine vision systems in industrial applications. IoT Analytics expects the machine vision market to grow at a compound annual growth rate of 8% from 2022 to 2027.

Three key technologies: Help machine vision more widely used

Machine vision technology has been around for more than 30 years, but recent technological shifts have provided a new impetus to further expand its adoption.

330190-040-00-00

Part1

330190-040-00-00 A shift in advanced camera technology. Cameras with resolutions of more than 45 million pixels are now not only better than the human eye, but can also shoot objects at extremely high speeds without distortion. The advancement of the camera is the introduction of “event-based vision sensors”, similar to the way the optic nerve processes information, event-based vision sensors capture images simply by detecting changes in the brightness of each pixel, and can work in darker environments or harsher weather conditions than traditional frame-based vision sensors.

Part2

330190-040-00-00 The transformation of artificial intelligence (AI) technology. The shift from rule-based machine vision (decisions based on predetermined parameters) to AI-based machine vision (decisions based on the output of the applicable MV model) is a highly impactful shift. Rule-based MV is “rigid” and only applies to quantifiable, clear, and very specific features, such as a certain scratch on a product, which answers a yes/no question. Ai-based MV can provide accurate results for non-quantifiable features, identify defects in a wider range of background and lighting Settings, and respond flexibly to changes in product appearance and defect types (such as dents or discoloration). Deep learning, as a more complex and powerful subset of artificial intelligence, is also increasingly being used in machine vision applications.

Part3

Technological progress and transformation of AI chips. The new generation of AI chips is more powerful and suitable for processing images and running AI-based machine vision algorithms. These advances have helped reduce deep learning training time from weeks to hours. Many smart camera machine vision systems are now equipped with powerful AI chips, such as the Nvidia Jetson Xavier NX module built into ADLINK’s NEON-2000-JNX series.