Expanded Use of Machine Vision
Published by : Industrial Automation
Considering the task of machine vision inside the past was thought of as typically a technology for tasks like inspection and identification. However, the imaginative and prescient-based techniques such as machine vision now play an expanding function in all fields, allowing all styles of interesting new applications.
Embedded vision is bringing a whole new variety of competencies to current products, combining both photographs seize and photo processing into one device. Embedded structures are lightweight, consume lower amounts of energy, feature lean designs, and create opportunities for new functionality, making them perfect for integration with current systems, in addition to products such as cell telephones and computers.
In heavy production operations, VEO Robotics is attempting to use gadget vision to allow hundreds of thousands of industrial robots which can be presently fenced off in factories to work safely around people. In addition, machine vision is also allowing advances in driverless cars, drones, and even in shopping, with the advent of shops like Amazon Go.
According to industry research, the global market for machine vision components, including cameras, frame grabbers, lighting, software, and hardware, will grow and reach USD 15 billion by 2025. Machine vision plays a vital role in factory automation and has extended its scope to the fields of security, entertainment, agriculture, and healthcare. While machine vision was once considered merely a replacement for human vision, today it is recognized as a driver for quality and productivity with the capability to capture multi-dimensional, non-visible information down to the micron.
Targeting growing niche markets such as robotics and autonomous vehicles will generate new revenue growth beyond traditional machine vision markets like electronics and automotive manufacturing. Another interesting development is the rise of independent machine vision cameras with built-in edge artificial intelligence (AI) processors. The conventional machine vision system has a more centralized processing architecture based on industrial PC.
According to ABI Research, deep learning-based machine vision can also incorporate data collected from various sensors, including LiDAR, radar, ultrasound, and magnetic field sensors. The rich set of data will provide further insight into other aspects of production processes. Deep learning algorithms deployed for machine vision can pick up unexpected product abnormalities or defects, providing flexibility and valuable insights to manufacturers. Such factors are expected to increase in innovation and advancement in machine vision to expand its use across the different industry verticals in the global market in the coming years.