Machine vision already proves its capabilities in the manufacturing industry, among others. It is the use of a camera or multiple cameras to look over and assess objects automatically, majorly in an industrial or production environment. Machine vision has come a long way when Electronic Sorting Machines (then located in New Jersey) in the 1930s offering food sorters based on using specific filters and photomultiplier detectors. With relentless innovation, the technology has evolved much more and continues to evolve.
As machine vision leverages optical instrumentation, digital video, electromagnetic sensing, image processing technology and others, its major objective is optical and non-contact sensing to obtain and evaluate a real image in order to deliver more information. The technology is widely used in monitoring and controlling a large number of applications.
As a rapidly growing branch of AI, machine vision typically identifies as a technology that can give machines sight and sense more relatively to human vision. Machine vision’s sight range is comparatively higher than human vision whose wavelength range between 380-740 nanometres. It is one of the latest trends that AI makes people to rethink on the way they look at machines nowadays.
Edge Technology in Machine Vision
Last December, ADLINK Technology collaborated with Intel and AWS to simplify AI at the edge for machine vision. The ADLINK AI at the Edge solution reportedly closes the loop on the full cycle of machine learning model building, from design and deployment to improvement, by automating edge computing processes. This can provide customers with a focus on developing applications without requiring advanced knowledge of data science and machine learning models.
According to Toby McClean, VP, IoT Innovation & Technology at ADLINK, who said then, “We’ve worked on multiple industrial use cases that benefit from AI at the edge, including a smart pallet solution that makes packages and pallets themselves intelligent so they can detect where they’re supposed to be, when they’re supposed to be there, in real-time.”
Traditionally, machine vision resides at the edge of the industrial network, meaning that machine vision systems operate independently. They may garner data from a plant network to achieve a task and share results with the network or downstream devices, but otherwise, these systems stand alone.
Edge technologies make it easy for data processing at or near the source of data generation and serve as a decentralized extension of the cloud, data center, or campus networks. The technologies have the ability to ease the integration of machine vision and motion control in local field sites in a matter of time. In the conventional visual inspection process, there were more challenges like there were no consistent criteria, measurable results, and present a lack of flexibility and higher labor costs. Using machine vision techniques, the automated optical inspection system is now gaining rapid traction in the inspection and quality assurance process.